{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "37dc741e-58ea-4557-acd9-e322ef7adf81",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "from lifelines import CoxTimeVaryingFitter\n",
    "from lifelines.utils import ConvergenceError\n",
    "from lifelines import KaplanMeierFitter\n",
    "from lifelines import AalenJohansenFitter\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.ticker import ScalarFormatter\n",
    "import matplotlib.ticker as mticker\n",
    "from matplotlib.ticker import PercentFormatter\n",
    "\n",
    "from scipy import stats\n",
    "\n",
    "from pathlib import Path\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "05a369cc-ab2a-445f-b9f4-43e4a4f18510",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "bba61cf2-8c7a-45cc-b3ae-3e01aa494b42",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>variable</th>\n",
       "      <th>N</th>\n",
       "      <th>observed</th>\n",
       "      <th>missing</th>\n",
       "      <th>observed_%</th>\n",
       "      <th>median_days [Q1;Q3]</th>\n",
       "      <th>min_days</th>\n",
       "      <th>max_days</th>\n",
       "      <th>≤0 n (%) among observed</th>\n",
       "      <th>=0 n (%) among observed</th>\n",
       "      <th>&gt;0 n (%) among observed</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>bmi_days</td>\n",
       "      <td>38806</td>\n",
       "      <td>32390</td>\n",
       "      <td>6416</td>\n",
       "      <td>83.5</td>\n",
       "      <td>0.1 [0.0; 87.9]</td>\n",
       "      <td>-2943.042361</td>\n",
       "      <td>3670.104861</td>\n",
       "      <td>4919 (15.2%)</td>\n",
       "      <td>15 (0.0%)</td>\n",
       "      <td>27471 (84.8%)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>creatinine_first_days</td>\n",
       "      <td>38806</td>\n",
       "      <td>30643</td>\n",
       "      <td>8163</td>\n",
       "      <td>79.0</td>\n",
       "      <td>0.7 [-0.2; 89.7]</td>\n",
       "      <td>-2971.213889</td>\n",
       "      <td>3297.394444</td>\n",
       "      <td>8271 (27.0%)</td>\n",
       "      <td>5 (0.0%)</td>\n",
       "      <td>22372 (73.0%)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>hb_glic_days</td>\n",
       "      <td>38806</td>\n",
       "      <td>22162</td>\n",
       "      <td>16644</td>\n",
       "      <td>57.1</td>\n",
       "      <td>7.9 [-34.0; 240.7]</td>\n",
       "      <td>-2945.538194</td>\n",
       "      <td>3295.972917</td>\n",
       "      <td>8127 (36.7%)</td>\n",
       "      <td>0 (0.0%)</td>\n",
       "      <td>14035 (63.3%)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                variable      N  observed  missing  observed_%  \\\n",
       "0               bmi_days  38806     32390     6416        83.5   \n",
       "1  creatinine_first_days  38806     30643     8163        79.0   \n",
       "2           hb_glic_days  38806     22162    16644        57.1   \n",
       "\n",
       "  median_days [Q1;Q3]     min_days     max_days ≤0 n (%) among observed  \\\n",
       "0     0.1 [0.0; 87.9] -2943.042361  3670.104861            4919 (15.2%)   \n",
       "1    0.7 [-0.2; 89.7] -2971.213889  3297.394444            8271 (27.0%)   \n",
       "2  7.9 [-34.0; 240.7] -2945.538194  3295.972917            8127 (36.7%)   \n",
       "\n",
       "  =0 n (%) among observed >0 n (%) among observed  \n",
       "0               15 (0.0%)           27471 (84.8%)  \n",
       "1                5 (0.0%)           22372 (73.0%)  \n",
       "2                0 (0.0%)           14035 (63.3%)  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('tbl_clean_v1.csv').iloc[:,1:]\n",
    "\n",
    "# Age -> Int64\n",
    "if \"age\" in df.columns:\n",
    "    df[\"age\"] = pd.to_numeric(df[\"age\"], errors=\"coerce\")\n",
    "    df[\"age\"] = df[\"age\"].round().astype(\"Int64\")\n",
    "\n",
    "# Gender -> male (1/0)\n",
    "if \"male\" not in df.columns and \"gender\" in df.columns:\n",
    "    df[\"male\"] = np.where(\n",
    "        df[\"gender\"].astype(str).str.strip().str.upper().eq(\"MALE\"),\n",
    "        1, 0\n",
    "    ).astype(\"int8\")\n",
    "\n",
    "# Insert index day\n",
    "df['index_day'] = 0\n",
    "\n",
    "# --- configure variables of interest ---\n",
    "days_cols = ['bmi_days', 'hb_glic_days', 'creatinine_first_days']\n",
    "\n",
    "def _median_iqr(x: pd.Series):\n",
    "    x = pd.to_numeric(x, errors='coerce').dropna()\n",
    "    if x.empty:\n",
    "        return np.nan, np.nan, np.nan\n",
    "    med = float(np.median(x))\n",
    "    q1, q3 = np.percentile(x, [25, 75])\n",
    "    return med, float(q1), float(q3)\n",
    "\n",
    "def summarize_days_vars(df: pd.DataFrame, cols: list[str]) -> pd.DataFrame:\n",
    "    rows = []\n",
    "    N = len(df)\n",
    "    for c in cols:\n",
    "        if c not in df.columns:\n",
    "            rows.append({\n",
    "                \"variable\": c, \"N\": N, \"observed\": 0, \"missing\": N, \"observed_%\": 0.0,\n",
    "                \"median_days [Q1;Q3]\": \"NA\", \"min_days\": \"NA\", \"max_days\": \"NA\",\n",
    "                \"≤0 n (%) among observed\": \"NA\", \"=0 n (%) among observed\": \"NA\",\n",
    "                \">0 n (%) among observed\": \"NA\"\n",
    "            })\n",
    "            continue\n",
    "\n",
    "        x = pd.to_numeric(df[c], errors='coerce')\n",
    "        obs = int(x.notna().sum())\n",
    "        miss = int(N - obs)\n",
    "        obs_pct = round(100 * obs / N, 1) if N else np.nan\n",
    "\n",
    "        med, q1, q3 = _median_iqr(x)\n",
    "        med_iqr_str = \"NA\" if np.isnan(med) else f\"{med:.1f} [{q1:.1f}; {q3:.1f}]\"\n",
    "\n",
    "        # distribuição relativa ao índice (dia 0), entre observados\n",
    "        n_le0 = int((x <= 0).sum(skipna=True)) if obs else 0\n",
    "        n_eq0 = int((x == 0).sum(skipna=True)) if obs else 0\n",
    "        n_gt0 = int((x > 0).sum(skipna=True))  if obs else 0\n",
    "        p = lambda n: (100 * n / obs) if obs else np.nan\n",
    "\n",
    "        row = {\n",
    "            \"variable\": c,\n",
    "            \"N\": N,\n",
    "            \"observed\": obs,\n",
    "            \"missing\": miss,\n",
    "            \"observed_%\": obs_pct,\n",
    "            \"median_days [Q1;Q3]\": med_iqr_str,\n",
    "            \"min_days\": \"NA\" if obs==0 else float(np.nanmin(x)),\n",
    "            \"max_days\": \"NA\" if obs==0 else float(np.nanmax(x)),\n",
    "            \"≤0 n (%) among observed\": \"NA\" if obs==0 else f\"{n_le0} ({p(n_le0):.1f}%)\",\n",
    "            \"=0 n (%) among observed\":  \"NA\" if obs==0 else f\"{n_eq0} ({p(n_eq0):.1f}%)\",\n",
    "            \">0 n (%) among observed\":  \"NA\" if obs==0 else f\"{n_gt0} ({p(n_gt0):.1f}%)\",\n",
    "        }\n",
    "        rows.append(row)\n",
    "\n",
    "    out = pd.DataFrame(rows)\n",
    "    # Ordenar por % observado (opcional)\n",
    "    out = out.sort_values(\"observed_%\", ascending=False).reset_index(drop=True)\n",
    "    return out\n",
    "\n",
    "summary_days = summarize_days_vars(df, days_cols)\n",
    "summary_days\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "ffa27911-0455-4570-b235-9c404ca549c0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>variable</th>\n",
       "      <th>observed</th>\n",
       "      <th>missing</th>\n",
       "      <th>observed_%</th>\n",
       "      <th>median_days [Q1;Q3]</th>\n",
       "      <th>min_days</th>\n",
       "      <th>max_days</th>\n",
       "      <th>≤0 n (%) among observed</th>\n",
       "      <th>=0 n (%) among observed</th>\n",
       "      <th>&gt;0 n (%) among observed</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>bmi_days</td>\n",
       "      <td>32390</td>\n",
       "      <td>6416</td>\n",
       "      <td>83.5</td>\n",
       "      <td>0.1 [0.0; 87.9]</td>\n",
       "      <td>-2943.042361</td>\n",
       "      <td>3670.104861</td>\n",
       "      <td>4919 (15.2%)</td>\n",
       "      <td>15 (0.0%)</td>\n",
       "      <td>27471 (84.8%)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>creatinine_first_days</td>\n",
       "      <td>30643</td>\n",
       "      <td>8163</td>\n",
       "      <td>79.0</td>\n",
       "      <td>0.7 [-0.2; 89.7]</td>\n",
       "      <td>-2971.213889</td>\n",
       "      <td>3297.394444</td>\n",
       "      <td>8271 (27.0%)</td>\n",
       "      <td>5 (0.0%)</td>\n",
       "      <td>22372 (73.0%)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>hb_glic_days</td>\n",
       "      <td>22162</td>\n",
       "      <td>16644</td>\n",
       "      <td>57.1</td>\n",
       "      <td>7.9 [-34.0; 240.7]</td>\n",
       "      <td>-2945.538194</td>\n",
       "      <td>3295.972917</td>\n",
       "      <td>8127 (36.7%)</td>\n",
       "      <td>0 (0.0%)</td>\n",
       "      <td>14035 (63.3%)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                variable  observed  missing  observed_% median_days [Q1;Q3]  \\\n",
       "0               bmi_days     32390     6416        83.5     0.1 [0.0; 87.9]   \n",
       "1  creatinine_first_days     30643     8163        79.0    0.7 [-0.2; 89.7]   \n",
       "2           hb_glic_days     22162    16644        57.1  7.9 [-34.0; 240.7]   \n",
       "\n",
       "      min_days     max_days ≤0 n (%) among observed =0 n (%) among observed  \\\n",
       "0 -2943.042361  3670.104861            4919 (15.2%)               15 (0.0%)   \n",
       "1 -2971.213889  3297.394444            8271 (27.0%)                5 (0.0%)   \n",
       "2 -2945.538194  3295.972917            8127 (36.7%)                0 (0.0%)   \n",
       "\n",
       "  >0 n (%) among observed  \n",
       "0           27471 (84.8%)  \n",
       "1           22372 (73.0%)  \n",
       "2           14035 (63.3%)  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "summary_days.drop(columns = 'N')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "102d5f46-5338-47c4-bb05-d28b5e149b83",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['patient_id', 'age', 'gender', 'bmi', 'bmi_days', 'hb_glic',\n",
       "       'hb_glic_days', 'creatinine_first', 'creatinine_first_days',\n",
       "       'creat_last_days', 'microalb', 'microalb_days', 'dm_nm', 'dm_days',\n",
       "       'ckd_non_dialysis', 'ace_arb_nm', 'ace_arb_days', 'statin_nm',\n",
       "       'statin_days', 'hypertension_days', 'cvd_days', 'last_visit',\n",
       "       'dialysis_days', 'death_days', 'male', 'index_day'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "ebedd579-e3cd-4262-a7bc-6198066239db",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df[['patient_id', 'age', 'male', 'bmi', 'hb_glic',\n",
    "        'creatinine_first', \n",
    "        'microalb', 'dm_nm', 'dm_days',\n",
    "       'ckd_non_dialysis', 'ace_arb_days', 'statin_nm',\n",
    "       'statin_days', 'hypertension_days', 'cvd_days', 'last_visit',\n",
    "       'dialysis_days', 'death_days', 'index_day']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "8a836127-2463-489a-8bbf-cae56b8aceef",
   "metadata": {},
   "outputs": [],
   "source": [
    "# -------------------\n",
    "# DM1 / DM2 by ICD\n",
    "# -------------------\n",
    "if \"dm_nm\" in df.columns:\n",
    "    codes = (\n",
    "        df[\"dm_nm\"]\n",
    "        .astype(str)\n",
    "        .str.extract(r\"\\[([A-Z]\\d{2}(?:\\.\\d)?)\\]\", expand=False)  # pega algo tipo E10, E10.9, O24.0\n",
    "        .str.upper()\n",
    "    )\n",
    "    dm1_mask = codes.str.startswith(\"E10\") | codes.eq(\"O24.0\")\n",
    "else:\n",
    "    dm1_mask = pd.Series(False, index=df.index)\n",
    "\n",
    "df[\"dm1\"] = dm1_mask.fillna(False).astype(\"int8\")\n",
    "df[\"dm2\"] = (1 - df[\"dm1\"]).astype(\"int8\")  # sua regra: se não DM1, vira DM2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "9c0cc4c2-46ec-4c40-886d-698912ff4121",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['patient_id', 'age', 'male', 'bmi', 'hb_glic', 'creatinine_first',\n",
       "       'microalb', 'dm_nm', 'dm_days', 'ckd_non_dialysis', 'ace_arb_days',\n",
       "       'statin_nm', 'statin_days', 'hypertension_days', 'cvd_days',\n",
       "       'last_visit', 'dialysis_days', 'death_days', 'index_day', 'dm1', 'dm2'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "7526e312-6cc2-4088-9348-3dc16d5c00da",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df[['patient_id', 'age', 'male', 'bmi', 'hb_glic',\n",
    "        'creatinine_first', \n",
    "        'dm2', \n",
    "       'ckd_non_dialysis', 'ace_arb_days', \n",
    "       'statin_days', 'hypertension_days','cvd_days', 'last_visit',\n",
    "       'dialysis_days', 'death_days', 'index_day']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "3fb84af1-f345-49d0-b99b-892092c00db7",
   "metadata": {},
   "outputs": [],
   "source": [
    "# get just dm2\n",
    "df = df[df.dm2 == 1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "c0613bb4-c37a-419e-8af0-8847aa95aeb0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "drop 232 patients without follow-up (no dialysis, death or last visit).\n",
      "Warning: 142 lines with end_time < 0 will be dropped.\n",
      "=== RESUMO INICIAL ===\n",
      "Pacientes únicos: 36246\n",
      "Eventos de diálise: 656\n",
      "Follow-up (mediana entre censurados), dias: 753.1\n",
      "\n",
      "Statin — categorias:\n",
      "  never         :  25474  | eventos: 237\n",
      "  start(>0)     :   8768  | eventos: 375\n",
      "  baseline(≤0)  :   2004  | eventos: 44\n",
      "\n",
      "Missing in principle covariables:\n",
      "hb_glic             15503\n",
      "creatinine_first     7782\n",
      "bmi                  5680\n",
      "age                     0\n",
      "male                    0\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "days_cols = [\n",
    "    'ckd_non_dialysis','ace_bra_days','statin_days','hypertension_days',\n",
    "    'cvd_days','last_visit','dialysis_days','death_days','index_day'\n",
    "]\n",
    "num_cols  = ['age','male','bmi','hb_glic','creatinine_first']\n",
    "\n",
    "for c in days_cols + num_cols:\n",
    "    if c in df.columns:\n",
    "        df[c] = pd.to_numeric(df[c], errors='coerce')\n",
    "\n",
    "assert 'index_day' in df.columns, \"Faltou index_day\"\n",
    "\n",
    "if not (df['index_day'].fillna(0)==0).all():\n",
    "    print(\"Aviso: há linhas com index_day != 0.\")\n",
    "\n",
    "# ==== 2) Define follow-up, event e censor ====\n",
    "# Censor = less them last visit and death (death = censor)\n",
    "df['censor_time'] = df[['last_visit','death_days']].min(axis=1, skipna=True)\n",
    "\n",
    "# Dialysis event occur if:\n",
    "# - dialysis date present AND (no censor before OR dialysis <= censor)\n",
    "df['event_dialysis'] = df['dialysis_days'].notna() & (\n",
    "    df['censor_time'].isna() | (df['dialysis_days'] <= df['censor_time'])\n",
    ")\n",
    "\n",
    "# Final follow up = event day (if occur) or time of censor\n",
    "df['end_time'] = np.where(df['event_dialysis'], df['dialysis_days'], df['censor_time'])\n",
    "\n",
    "# Drop missing follow-up \n",
    "mask_follow = df['end_time'].notna()\n",
    "n_drop_nofup = (~mask_follow).sum()\n",
    "df = df.loc[mask_follow].copy()\n",
    "if n_drop_nofup > 0:\n",
    "    print(f\"drop {n_drop_nofup} patients without follow-up (no dialysis, death or last visit).\")\n",
    "\n",
    "# Optional: drop negative time /impossible\n",
    "neg_end = (df['end_time'] < 0).sum()\n",
    "if neg_end > 0:\n",
    "    print(f\"Warning: {neg_end} lines with end_time < 0 will be dropped.\")\n",
    "    df = df.loc[df['end_time'] >= 0].copy()\n",
    "\n",
    "# ==== 3) Flags of baseline (occur until index_day) ====\n",
    "def baseline_flag(x):\n",
    "    return np.where(np.isfinite(x) & (x <= 0), 1, 0)\n",
    "\n",
    "df['hypertension_baseline']  = baseline_flag(df['hypertension_days'])\n",
    "df['cvd_baseline']  = baseline_flag(df['cvd_days'])\n",
    "df['ckd_baseline']  = baseline_flag(df['ckd_non_dialysis'])\n",
    "df['ace_arb_base'] = baseline_flag(df['ace_arb_days'])\n",
    "\n",
    "# ==== 4) Resumo rápido da coorte ====\n",
    "n_patients = df['patient_id'].nunique()\n",
    "n_events   = int(df['event_dialysis'].sum())\n",
    "follow_cens = df.loc[~df['event_dialysis'], 'end_time']\n",
    "median_fu_cens = float(np.nanmedian(follow_cens)) if not follow_cens.empty else np.nan\n",
    "\n",
    "# categorias de estatina\n",
    "statin_cat = pd.Series(np.select(\n",
    "    [\n",
    "        df['statin_days'].isna(),\n",
    "        df['statin_days'] <= 0,\n",
    "        df['statin_days'] > 0\n",
    "    ],\n",
    "    ['never', 'baseline(≤0)', 'start(>0)'],\n",
    "    default='unknown'\n",
    "), index=df.index)\n",
    "\n",
    "cat_counts = statin_cat.value_counts(dropna=False)\n",
    "cat_events = df.groupby(statin_cat)['event_dialysis'].sum().reindex(cat_counts.index).fillna(0).astype(int)\n",
    "\n",
    "print(\"=== RESUMO INICIAL ===\")\n",
    "print(f\"Pacientes únicos: {n_patients}\")\n",
    "print(f\"Eventos de diálise: {n_events}\")\n",
    "print(f\"Follow-up (mediana entre censurados), dias: {median_fu_cens:.1f}\")\n",
    "print(\"\\nStatin — categorias:\")\n",
    "for k, v in cat_counts.items():\n",
    "    print(f\"  {k:14s}: {int(v):6d}  | eventos: {int(cat_events.loc[k])}\")\n",
    "\n",
    "print(\"\\nMissing in principle covariables:\")\n",
    "miss = df[['age','male','bmi','hb_glic','creatinine_first']].isna().sum()\n",
    "print(miss.sort_values(ascending=False))\n",
    "\n",
    "# df agora está pronto para construir o dataset time-varying (PASSO 2)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "b4df8470-c77f-4369-b9a0-3847c2445924",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "=== TV (time-varying) — Resumo ===\n",
      "linhas (intervalos): 39943\n",
      "pacientes: 32125\n",
      "eventos marcados: 656\n",
      "            sum  count\n",
      "statin_exp            \n",
      "nao exp     352  30285\n",
      "exposto     304   9658\n",
      "\n",
      "Taxas por 1000 pessoa-anos (descritivo):\n",
      "             events   person_days  rate_1000py\n",
      "statin_exp                                   \n",
      "0              352  2.850665e+07     4.510106\n",
      "1              304  9.022337e+06    12.306789\n",
      "\n",
      "Faltantes no tv:\n",
      " hb_glic             14157\n",
      "creatinine_first     6097\n",
      "bmi                  4704\n",
      "dtype: int64\n",
      "\n",
      "Complete case: 24195 intervalos | pacs 18455 | eventos 570\n",
      "Input: 39943 intervals | pacs 32125 | eventos 656\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_3650957/4177302659.py:89: DeprecationWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.\n",
      "  overlap = tv.groupby('patient_id', group_keys=False).apply(_overlap_mask)\n"
     ]
    }
   ],
   "source": [
    "# ===== PASSO 2 — Data long com exposição time-varying =====\n",
    "# Pré-requisito: df já contém end_time, event_dialysis e as flags *_baseline do PASSO 1.\n",
    "\n",
    "cont_covs = ['age','bmi','hb_glic','creatinine_first']\n",
    "bin_covs  = ['male','hypertension_baseline','cvd_baseline','ckd_baseline','ace_arb_base']\n",
    "\n",
    "rows = []\n",
    "n_dup_events = 0\n",
    "\n",
    "for i, r in df.iterrows():\n",
    "    stop = float(r['end_time'])\n",
    "    if not np.isfinite(stop) or stop <= 0:\n",
    "        continue\n",
    "\n",
    "    start0 = 0.0\n",
    "    sd = r['statin_days']           # dia de início de estatina (NaN = nunca)\n",
    "    dd = r['dialysis_days'] if pd.notna(r['dialysis_days']) else np.inf\n",
    "    ev  = bool(r['event_dialysis'])\n",
    "\n",
    "    # helper: monta uma linha\n",
    "    def add_interval(s, e, exp_flag, event_here):\n",
    "        rows.append({\n",
    "            'patient_id': r['patient_id'],\n",
    "            'start': float(s),\n",
    "            'stop': float(e),\n",
    "            'statin_exp': int(exp_flag),\n",
    "            'event': int(event_here),\n",
    "            # covariáveis (baseline, carregadas p/ todos os intervalos)\n",
    "            'age': r['age'],\n",
    "            'male': r['male'],\n",
    "            'bmi': r.get('bmi', np.nan),\n",
    "            'hb_glic': r.get('hb_glic', np.nan),\n",
    "            'creatinine_first': r.get('creatinine_first', np.nan),\n",
    "            'hypertension_baseline': r['hypertension_baseline'],\n",
    "            'cvd_baseline': r['cvd_baseline'],\n",
    "            'ckd_baseline': r['ckd_baseline'],\n",
    "            'ace_arb_base': r['ace_arb_base'],\n",
    "        })\n",
    "\n",
    "    if not np.isfinite(sd):\n",
    "        # nunca exposto\n",
    "        add_interval(start0, stop, 0, ev and (dd <= stop))\n",
    "    elif sd <= 0:\n",
    "        # exposto já no baseline\n",
    "        add_interval(start0, stop, 1, ev and (dd <= stop))\n",
    "    else:\n",
    "        # iniciador após baseline\n",
    "        split = float(sd)\n",
    "        if split >= stop:\n",
    "            # iniciou depois do fim do seguimento -> todo período não exposto\n",
    "            add_interval(start0, stop, 0, ev and (dd <= stop))\n",
    "        else:\n",
    "            # trecho não exposto\n",
    "            add_interval(start0, split, 0, ev and (dd <= split))\n",
    "            # trecho exposto\n",
    "            add_interval(split, stop, 1, ev and (dd > split) and (dd <= stop))\n",
    "\n",
    "tv = pd.DataFrame(rows)\n",
    "\n",
    "# Clean intervals e order\n",
    "tv = tv[(tv['stop'] > tv['start'])].copy()\n",
    "tv = tv.sort_values(['patient_id','start','stop']).reset_index(drop=True)\n",
    "\n",
    "# garantee max 1 event per pacient\n",
    "evt_counts = tv.groupby('patient_id')['event'].sum()\n",
    "bad_ids = evt_counts[evt_counts > 1].index\n",
    "if len(bad_ids) > 0:\n",
    "    # keep first event and zero others\n",
    "    def keep_first_event(g):\n",
    "        if g['event'].sum() <= 1:\n",
    "            return g\n",
    "        # zero for all e put 1 in the first interval with the event\n",
    "        g = g.copy()\n",
    "        idx_first = g.index[g['event'] == 1][0]\n",
    "        g.loc[:, 'event'] = 0\n",
    "        g.loc[idx_first, 'event'] = 1\n",
    "        return g\n",
    "    tv = tv.groupby('patient_id', group_keys=False).apply(keep_first_event)\n",
    "\n",
    "# check superposition (start of i+1 < stop do i) e remove if exist\n",
    "def _overlap_mask(g):\n",
    "    starts = g['start'].to_numpy()\n",
    "    stops  = g['stop'].to_numpy()\n",
    "    ov = np.zeros(len(g), dtype=bool)\n",
    "    if len(g) > 1:\n",
    "        ov[1:] = starts[1:] < stops[:-1]\n",
    "    return pd.Series(ov, index=g.index)\n",
    "\n",
    "overlap = tv.groupby('patient_id', group_keys=False).apply(_overlap_mask)\n",
    "if overlap.any():\n",
    "    print(f\"Aviso: removendo {int(overlap.sum())} linhas com intervalos sobrepostos.\")\n",
    "    tv = tv.loc[~overlap].copy()\n",
    "\n",
    "# ===== Resume and checks =====\n",
    "print(\"=== TV (time-varying) — Resumo ===\")\n",
    "print(\"linhas (intervalos):\", len(tv))\n",
    "print(\"pacientes:\", tv['patient_id'].nunique())\n",
    "print(\"eventos marcados:\", int(tv['event'].sum()))\n",
    "print(tv.groupby('statin_exp')['event'].agg(['sum','count']).rename(index={0:'nao exp',1:'exposto'}))\n",
    "\n",
    "# taxa bruta por 1000 pessoa-anos (descritivo)\n",
    "tv['ptime'] = tv['stop'] - tv['start']\n",
    "rates = tv.groupby('statin_exp').agg(events=('event','sum'),\n",
    "                                     person_days=('ptime','sum'))\n",
    "rates['rate_1000py'] = (rates['events'] / (rates['person_days']/365.25)) * 1000\n",
    "print(\"\\nTaxas por 1000 pessoa-anos (descritivo):\\n\", rates)\n",
    "\n",
    "# missing of covariables (now in tv)\n",
    "miss_tv = tv[cont_covs + bin_covs].isna().sum().sort_values(ascending=False)\n",
    "print(\"\\nFaltantes no tv:\\n\", miss_tv[miss_tv > 0])\n",
    "\n",
    "# ===== Datasets read to models =====\n",
    "# 2A) Complete case\n",
    "tv_cc = tv.dropna(subset=cont_covs).copy()\n",
    "for c in bin_covs + ['statin_exp','event']:\n",
    "    tv_cc[c] = tv_cc[c].astype(int)\n",
    "\n",
    "print(f\"\\nComplete case: {len(tv_cc)} intervalos | pacs {tv_cc['patient_id'].nunique()} | eventos {int(tv_cc['event'].sum())}\")\n",
    "\n",
    "# 2B) Input (median in continues) — max in missings\n",
    "medians = {c: df[c].median(skipna=True) for c in cont_covs}\n",
    "tv_imp = tv.copy()\n",
    "for c in cont_covs:\n",
    "    tv_imp[c] = tv_imp[c].fillna(medians[c])\n",
    "for c in bin_covs + ['statin_exp','event']:\n",
    "    tv_imp[c] = tv_imp[c].astype(int)\n",
    "\n",
    "print(f\"Input: {len(tv_imp)} intervals | pacs {tv_imp['patient_id'].nunique()} | eventos {int(tv_imp['event'].sum())}\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "31187d3f-af92-4899-b553-1eae7cf8c3f6",
   "metadata": {},
   "outputs": [],
   "source": [
    "X = tv_imp.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "b4eb1708-5a8a-4b06-875f-165fe82a6f5c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# covariáveis do modelo\n",
    "covars = ['statin_exp','age','male','bmi','hb_glic','creatinine_first',\n",
    "          'hypertension_baseline','cvd_baseline','ckd_baseline','ace_arb_base']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "264cb7db-8fb0-4325-85c7-2bc1938ecc64",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[OK] ajusted model with penalizer=0.5\n"
     ]
    }
   ],
   "source": [
    "# COX analysis --- WITHOUTROBUST, WITH PENALIZER ---\n",
    "fitted = False\n",
    "last_err = None\n",
    "\n",
    "for pen in [0.5, 1.0, 2.0]:\n",
    "    try:\n",
    "        ctv = CoxTimeVaryingFitter(penalizer=pen)\n",
    "        ctv.fit(\n",
    "            X[['patient_id','start','stop','event'] + covars],\n",
    "            id_col='patient_id', start_col='start', stop_col='stop', event_col='event',\n",
    "            show_progress=False\n",
    "        )\n",
    "        print(f\"[OK] ajusted model with penalizer={pen}\")\n",
    "        fitted = True\n",
    "        break\n",
    "    except ConvergenceError as e:\n",
    "        last_err = e\n",
    "        print(f\"[Warning] Convergence failed with penalizer={pen}. Trying ...\")\n",
    "    except Exception as e:\n",
    "        last_err = e\n",
    "        print(f\"[Error] penalizer={pen}: {e}\")\n",
    "        break"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "2858c5b4-b87c-4b37-b0ce-a2d34f481433",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>model</th>\n",
       "      <td>lifelines.CoxTimeVaryingFitter</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>event col</th>\n",
       "      <td>'event'</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>penalizer</th>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>number of subjects</th>\n",
       "      <td>32125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>number of periods</th>\n",
       "      <td>39943</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>number of events</th>\n",
       "      <td>656</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>partial log-likelihood</th>\n",
       "      <td>-6323.37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>time fit was run</th>\n",
       "      <td>2025-09-27 15:15:26 UTC</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div><table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th style=\"min-width: 12px;\"></th>\n",
       "      <th style=\"min-width: 12px;\">coef</th>\n",
       "      <th style=\"min-width: 12px;\">exp(coef)</th>\n",
       "      <th style=\"min-width: 12px;\">se(coef)</th>\n",
       "      <th style=\"min-width: 12px;\">coef lower 95%</th>\n",
       "      <th style=\"min-width: 12px;\">coef upper 95%</th>\n",
       "      <th style=\"min-width: 12px;\">exp(coef) lower 95%</th>\n",
       "      <th style=\"min-width: 12px;\">exp(coef) upper 95%</th>\n",
       "      <th style=\"min-width: 12px;\">cmp to</th>\n",
       "      <th style=\"min-width: 12px;\">z</th>\n",
       "      <th style=\"min-width: 12px;\">p</th>\n",
       "      <th style=\"min-width: 12px;\">-log2(p)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>statin_exp</th>\n",
       "      <td>0.04</td>\n",
       "      <td>1.04</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.07</td>\n",
       "      <td>1.01</td>\n",
       "      <td>1.08</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2.61</td>\n",
       "      <td>0.01</td>\n",
       "      <td>6.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>age</th>\n",
       "      <td>0.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>-0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.91</td>\n",
       "      <td>0.06</td>\n",
       "      <td>4.16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>male</th>\n",
       "      <td>0.02</td>\n",
       "      <td>1.02</td>\n",
       "      <td>0.01</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>0.04</td>\n",
       "      <td>0.99</td>\n",
       "      <td>1.05</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.21</td>\n",
       "      <td>0.23</td>\n",
       "      <td>2.13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>bmi</th>\n",
       "      <td>0.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>-0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.66</td>\n",
       "      <td>0.51</td>\n",
       "      <td>0.97</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>hb_glic</th>\n",
       "      <td>0.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.01</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.99</td>\n",
       "      <td>1.02</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.45</td>\n",
       "      <td>1.14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>creatinine_first</th>\n",
       "      <td>0.16</td>\n",
       "      <td>1.17</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.14</td>\n",
       "      <td>0.18</td>\n",
       "      <td>1.15</td>\n",
       "      <td>1.20</td>\n",
       "      <td>0.00</td>\n",
       "      <td>14.51</td>\n",
       "      <td>&lt;0.005</td>\n",
       "      <td>156.15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>hypertension_baseline</th>\n",
       "      <td>0.02</td>\n",
       "      <td>1.02</td>\n",
       "      <td>0.01</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.99</td>\n",
       "      <td>1.05</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.59</td>\n",
       "      <td>0.11</td>\n",
       "      <td>3.15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cvd_baseline</th>\n",
       "      <td>0.04</td>\n",
       "      <td>1.04</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.07</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.07</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.98</td>\n",
       "      <td>0.05</td>\n",
       "      <td>4.40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ckd_baseline</th>\n",
       "      <td>0.31</td>\n",
       "      <td>1.37</td>\n",
       "      <td>0.04</td>\n",
       "      <td>0.23</td>\n",
       "      <td>0.40</td>\n",
       "      <td>1.26</td>\n",
       "      <td>1.49</td>\n",
       "      <td>0.00</td>\n",
       "      <td>7.24</td>\n",
       "      <td>&lt;0.005</td>\n",
       "      <td>41.04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ace_arb_base</th>\n",
       "      <td>0.02</td>\n",
       "      <td>1.02</td>\n",
       "      <td>0.03</td>\n",
       "      <td>-0.04</td>\n",
       "      <td>0.07</td>\n",
       "      <td>0.96</td>\n",
       "      <td>1.08</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.59</td>\n",
       "      <td>0.56</td>\n",
       "      <td>0.85</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table><br><div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Partial AIC</th>\n",
       "      <td>12666.73</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>log-likelihood ratio test</th>\n",
       "      <td>280.86 on 10 df</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>-log2(p) of ll-ratio test</th>\n",
       "      <td>178.60</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/latex": [
       "\\begin{tabular}{lrrrrrrrrrrr}\n",
       " & coef & exp(coef) & se(coef) & coef lower 95% & coef upper 95% & exp(coef) lower 95% & exp(coef) upper 95% & cmp to & z & p & -log2(p) \\\\\n",
       "covariate &  &  &  &  &  &  &  &  &  &  &  \\\\\n",
       "statin_exp & 0.04 & 1.04 & 0.02 & 0.01 & 0.07 & 1.01 & 1.08 & 0.00 & 2.61 & 0.01 & 6.80 \\\\\n",
       "age & 0.00 & 1.00 & 0.00 & -0.00 & 0.00 & 1.00 & 1.00 & 0.00 & 1.91 & 0.06 & 4.16 \\\\\n",
       "male & 0.02 & 1.02 & 0.01 & -0.01 & 0.04 & 0.99 & 1.05 & 0.00 & 1.21 & 0.23 & 2.13 \\\\\n",
       "bmi & 0.00 & 1.00 & 0.00 & -0.00 & 0.00 & 1.00 & 1.00 & 0.00 & 0.66 & 0.51 & 0.97 \\\\\n",
       "hb_glic & 0.00 & 1.00 & 0.01 & -0.01 & 0.02 & 0.99 & 1.02 & 0.00 & 0.75 & 0.45 & 1.14 \\\\\n",
       "creatinine_first & 0.16 & 1.17 & 0.01 & 0.14 & 0.18 & 1.15 & 1.20 & 0.00 & 14.51 & 0.00 & 156.15 \\\\\n",
       "hypertension_baseline & 0.02 & 1.02 & 0.01 & -0.01 & 0.05 & 0.99 & 1.05 & 0.00 & 1.59 & 0.11 & 3.15 \\\\\n",
       "cvd_baseline & 0.04 & 1.04 & 0.02 & 0.00 & 0.07 & 1.00 & 1.07 & 0.00 & 1.98 & 0.05 & 4.40 \\\\\n",
       "ckd_baseline & 0.31 & 1.37 & 0.04 & 0.23 & 0.40 & 1.26 & 1.49 & 0.00 & 7.24 & 0.00 & 41.04 \\\\\n",
       "ace_arb_base & 0.02 & 1.02 & 0.03 & -0.04 & 0.07 & 0.96 & 1.08 & 0.00 & 0.59 & 0.56 & 0.85 \\\\\n",
       "\\end{tabular}\n"
      ],
      "text/plain": [
       "<lifelines.CoxTimeVaryingFitter: fitted with 39943 periods, 32125 subjects, 656 events>\n",
       "         event col = 'event'\n",
       "         penalizer = 0.5\n",
       "number of subjects = 32125\n",
       " number of periods = 39943\n",
       "  number of events = 656\n",
       "partial log-likelihood = -6323.37\n",
       "  time fit was run = 2025-09-27 15:15:26 UTC\n",
       "\n",
       "---\n",
       "                       coef exp(coef)  se(coef)  coef lower 95%  coef upper 95% exp(coef) lower 95% exp(coef) upper 95%\n",
       "covariate                                                                                                              \n",
       "statin_exp             0.04      1.04      0.02            0.01            0.07                1.01                1.08\n",
       "age                    0.00      1.00      0.00           -0.00            0.00                1.00                1.00\n",
       "male                   0.02      1.02      0.01           -0.01            0.04                0.99                1.05\n",
       "bmi                    0.00      1.00      0.00           -0.00            0.00                1.00                1.00\n",
       "hb_glic                0.00      1.00      0.01           -0.01            0.02                0.99                1.02\n",
       "creatinine_first       0.16      1.17      0.01            0.14            0.18                1.15                1.20\n",
       "hypertension_baseline  0.02      1.02      0.01           -0.01            0.05                0.99                1.05\n",
       "cvd_baseline           0.04      1.04      0.02            0.00            0.07                1.00                1.07\n",
       "ckd_baseline           0.31      1.37      0.04            0.23            0.40                1.26                1.49\n",
       "ace_arb_base           0.02      1.02      0.03           -0.04            0.07                0.96                1.08\n",
       "\n",
       "                       cmp to     z      p  -log2(p)\n",
       "covariate                                           \n",
       "statin_exp               0.00  2.61   0.01      6.80\n",
       "age                      0.00  1.91   0.06      4.16\n",
       "male                     0.00  1.21   0.23      2.13\n",
       "bmi                      0.00  0.66   0.51      0.97\n",
       "hb_glic                  0.00  0.75   0.45      1.14\n",
       "creatinine_first         0.00 14.51 <0.005    156.15\n",
       "hypertension_baseline    0.00  1.59   0.11      3.15\n",
       "cvd_baseline             0.00  1.98   0.05      4.40\n",
       "ckd_baseline             0.00  7.24 <0.005     41.04\n",
       "ace_arb_base             0.00  0.59   0.56      0.85\n",
       "---\n",
       "Partial AIC = 12666.73\n",
       "log-likelihood ratio test = 280.86 on 10 df\n",
       "-log2(p) of ll-ratio test = 178.60"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "== Statin effect (ajusted) ==\n",
      "HR = 1.043 (IC95% 1.011–1.077), p = 0.009001\n"
     ]
    }
   ],
   "source": [
    "if not fitted:\n",
    "    # fallback: remove continuous covariable more unstable and try againo\n",
    "    fallback_covars = ['statin_exp','age','male','bmi','creatinine_first',\n",
    "                       'hypertension_baseline','cvd_baseline','ckd_baseline','ace_arb_base']  # sem hb_glic\n",
    "    print(\"\\n[Fallback] Removing 'hb_glic' e trying again with penalizer=1.0\")\n",
    "    try:\n",
    "        ctv = CoxTimeVaryingFitter(penalizer=1.0)\n",
    "        ctv.fit(\n",
    "            X[['patient_id','start','stop','event'] + fallback_covars],\n",
    "            id_col='patient_id', start_col='start', stop_col='stop', event_col='event',\n",
    "            show_progress=False\n",
    "        )\n",
    "        covars = fallback_covars\n",
    "        fitted = True\n",
    "    except Exception as e:\n",
    "        raise RuntimeError(f\"Failed in fallback. Last error lifelines: {last_err}\") from e\n",
    "\n",
    "# resumo\n",
    "ctv.print_summary()\n",
    "\n",
    "# HR da estatina\n",
    "summ = ctv.summary\n",
    "hr   = summ.loc['statin_exp', 'exp(coef)']\n",
    "lo95 = summ.loc['statin_exp', 'exp(coef) lower 95%']\n",
    "hi95 = summ.loc['statin_exp', 'exp(coef) upper 95%']\n",
    "pval = summ.loc['statin_exp', 'p']\n",
    "print(f\"\\n== Statin effect (ajusted) ==\\nHR = {hr:.3f} (IC95% {lo95:.3f}–{hi95:.3f}), p = {pval:.4g}\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "9dda4a54-97e3-4c28-9369-a44cd5303977",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>model</th>\n",
       "      <td>lifelines.CoxTimeVaryingFitter</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>event col</th>\n",
       "      <td>'event'</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>penalizer</th>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>number of subjects</th>\n",
       "      <td>32125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>number of periods</th>\n",
       "      <td>77793</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>number of events</th>\n",
       "      <td>656</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>partial log-likelihood</th>\n",
       "      <td>-6394.29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>time fit was run</th>\n",
       "      <td>2025-09-27 15:18:05 UTC</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div><table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th style=\"min-width: 12px;\"></th>\n",
       "      <th style=\"min-width: 12px;\">coef</th>\n",
       "      <th style=\"min-width: 12px;\">exp(coef)</th>\n",
       "      <th style=\"min-width: 12px;\">se(coef)</th>\n",
       "      <th style=\"min-width: 12px;\">coef lower 95%</th>\n",
       "      <th style=\"min-width: 12px;\">coef upper 95%</th>\n",
       "      <th style=\"min-width: 12px;\">exp(coef) lower 95%</th>\n",
       "      <th style=\"min-width: 12px;\">exp(coef) upper 95%</th>\n",
       "      <th style=\"min-width: 12px;\">cmp to</th>\n",
       "      <th style=\"min-width: 12px;\">z</th>\n",
       "      <th style=\"min-width: 12px;\">p</th>\n",
       "      <th style=\"min-width: 12px;\">-log2(p)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>exp_0_1y</th>\n",
       "      <td>0.02</td>\n",
       "      <td>1.02</td>\n",
       "      <td>0.02</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.99</td>\n",
       "      <td>1.06</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.14</td>\n",
       "      <td>0.25</td>\n",
       "      <td>1.98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>exp_1_3y</th>\n",
       "      <td>0.01</td>\n",
       "      <td>1.01</td>\n",
       "      <td>0.02</td>\n",
       "      <td>-0.02</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.98</td>\n",
       "      <td>1.05</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.70</td>\n",
       "      <td>0.49</td>\n",
       "      <td>1.04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>exp_3p</th>\n",
       "      <td>0.03</td>\n",
       "      <td>1.03</td>\n",
       "      <td>0.02</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>0.07</td>\n",
       "      <td>0.99</td>\n",
       "      <td>1.07</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.26</td>\n",
       "      <td>0.21</td>\n",
       "      <td>2.26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>age</th>\n",
       "      <td>0.01</td>\n",
       "      <td>1.01</td>\n",
       "      <td>0.01</td>\n",
       "      <td>-0.00</td>\n",
       "      <td>0.02</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.02</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.41</td>\n",
       "      <td>0.16</td>\n",
       "      <td>2.65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>bmi</th>\n",
       "      <td>0.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>-0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.49</td>\n",
       "      <td>0.62</td>\n",
       "      <td>0.68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>hb_glic</th>\n",
       "      <td>0.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>-0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.55</td>\n",
       "      <td>0.58</td>\n",
       "      <td>0.79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>creatinine_first</th>\n",
       "      <td>0.02</td>\n",
       "      <td>1.03</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.03</td>\n",
       "      <td>1.02</td>\n",
       "      <td>1.03</td>\n",
       "      <td>0.00</td>\n",
       "      <td>9.73</td>\n",
       "      <td>&lt;0.005</td>\n",
       "      <td>71.94</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>male</th>\n",
       "      <td>0.01</td>\n",
       "      <td>1.01</td>\n",
       "      <td>0.01</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>0.03</td>\n",
       "      <td>0.99</td>\n",
       "      <td>1.03</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.90</td>\n",
       "      <td>0.37</td>\n",
       "      <td>1.44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>hypertension_baseline</th>\n",
       "      <td>0.01</td>\n",
       "      <td>1.01</td>\n",
       "      <td>0.01</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>0.03</td>\n",
       "      <td>0.99</td>\n",
       "      <td>1.03</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.17</td>\n",
       "      <td>0.24</td>\n",
       "      <td>2.05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cvd_baseline</th>\n",
       "      <td>0.02</td>\n",
       "      <td>1.02</td>\n",
       "      <td>0.01</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.99</td>\n",
       "      <td>1.05</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.52</td>\n",
       "      <td>0.13</td>\n",
       "      <td>2.96</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ckd_baseline</th>\n",
       "      <td>0.19</td>\n",
       "      <td>1.21</td>\n",
       "      <td>0.03</td>\n",
       "      <td>0.13</td>\n",
       "      <td>0.26</td>\n",
       "      <td>1.14</td>\n",
       "      <td>1.30</td>\n",
       "      <td>0.00</td>\n",
       "      <td>5.71</td>\n",
       "      <td>&lt;0.005</td>\n",
       "      <td>26.38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ace_arb_base</th>\n",
       "      <td>0.01</td>\n",
       "      <td>1.01</td>\n",
       "      <td>0.02</td>\n",
       "      <td>-0.03</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.97</td>\n",
       "      <td>1.05</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.46</td>\n",
       "      <td>0.64</td>\n",
       "      <td>0.63</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table><br><div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Partial AIC</th>\n",
       "      <td>12812.59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>log-likelihood ratio test</th>\n",
       "      <td>139.00 on 12 df</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>-log2(p) of ll-ratio test</th>\n",
       "      <td>76.47</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/latex": [
       "\\begin{tabular}{lrrrrrrrrrrr}\n",
       " & coef & exp(coef) & se(coef) & coef lower 95% & coef upper 95% & exp(coef) lower 95% & exp(coef) upper 95% & cmp to & z & p & -log2(p) \\\\\n",
       "covariate &  &  &  &  &  &  &  &  &  &  &  \\\\\n",
       "exp_0_1y & 0.02 & 1.02 & 0.02 & -0.01 & 0.05 & 0.99 & 1.06 & 0.00 & 1.14 & 0.25 & 1.98 \\\\\n",
       "exp_1_3y & 0.01 & 1.01 & 0.02 & -0.02 & 0.05 & 0.98 & 1.05 & 0.00 & 0.70 & 0.49 & 1.04 \\\\\n",
       "exp_3p & 0.03 & 1.03 & 0.02 & -0.01 & 0.07 & 0.99 & 1.07 & 0.00 & 1.26 & 0.21 & 2.26 \\\\\n",
       "age & 0.01 & 1.01 & 0.01 & -0.00 & 0.02 & 1.00 & 1.02 & 0.00 & 1.41 & 0.16 & 2.65 \\\\\n",
       "bmi & 0.00 & 1.00 & 0.00 & -0.00 & 0.00 & 1.00 & 1.00 & 0.00 & 0.49 & 0.62 & 0.68 \\\\\n",
       "hb_glic & 0.00 & 1.00 & 0.00 & -0.00 & 0.00 & 1.00 & 1.00 & 0.00 & 0.55 & 0.58 & 0.79 \\\\\n",
       "creatinine_first & 0.02 & 1.03 & 0.00 & 0.02 & 0.03 & 1.02 & 1.03 & 0.00 & 9.73 & 0.00 & 71.94 \\\\\n",
       "male & 0.01 & 1.01 & 0.01 & -0.01 & 0.03 & 0.99 & 1.03 & 0.00 & 0.90 & 0.37 & 1.44 \\\\\n",
       "hypertension_baseline & 0.01 & 1.01 & 0.01 & -0.01 & 0.03 & 0.99 & 1.03 & 0.00 & 1.17 & 0.24 & 2.05 \\\\\n",
       "cvd_baseline & 0.02 & 1.02 & 0.01 & -0.01 & 0.05 & 0.99 & 1.05 & 0.00 & 1.52 & 0.13 & 2.96 \\\\\n",
       "ckd_baseline & 0.19 & 1.21 & 0.03 & 0.13 & 0.26 & 1.14 & 1.30 & 0.00 & 5.71 & 0.00 & 26.38 \\\\\n",
       "ace_arb_base & 0.01 & 1.01 & 0.02 & -0.03 & 0.05 & 0.97 & 1.05 & 0.00 & 0.46 & 0.64 & 0.63 \\\\\n",
       "\\end{tabular}\n"
      ],
      "text/plain": [
       "<lifelines.CoxTimeVaryingFitter: fitted with 77793 periods, 32125 subjects, 656 events>\n",
       "         event col = 'event'\n",
       "         penalizer = 0.5\n",
       "number of subjects = 32125\n",
       " number of periods = 77793\n",
       "  number of events = 656\n",
       "partial log-likelihood = -6394.29\n",
       "  time fit was run = 2025-09-27 15:18:05 UTC\n",
       "\n",
       "---\n",
       "                       coef exp(coef)  se(coef)  coef lower 95%  coef upper 95% exp(coef) lower 95% exp(coef) upper 95%\n",
       "covariate                                                                                                              \n",
       "exp_0_1y               0.02      1.02      0.02           -0.01            0.05                0.99                1.06\n",
       "exp_1_3y               0.01      1.01      0.02           -0.02            0.05                0.98                1.05\n",
       "exp_3p                 0.03      1.03      0.02           -0.01            0.07                0.99                1.07\n",
       "age                    0.01      1.01      0.01           -0.00            0.02                1.00                1.02\n",
       "bmi                    0.00      1.00      0.00           -0.00            0.00                1.00                1.00\n",
       "hb_glic                0.00      1.00      0.00           -0.00            0.00                1.00                1.00\n",
       "creatinine_first       0.02      1.03      0.00            0.02            0.03                1.02                1.03\n",
       "male                   0.01      1.01      0.01           -0.01            0.03                0.99                1.03\n",
       "hypertension_baseline  0.01      1.01      0.01           -0.01            0.03                0.99                1.03\n",
       "cvd_baseline           0.02      1.02      0.01           -0.01            0.05                0.99                1.05\n",
       "ckd_baseline           0.19      1.21      0.03            0.13            0.26                1.14                1.30\n",
       "ace_arb_base           0.01      1.01      0.02           -0.03            0.05                0.97                1.05\n",
       "\n",
       "                       cmp to    z      p  -log2(p)\n",
       "covariate                                          \n",
       "exp_0_1y                 0.00 1.14   0.25      1.98\n",
       "exp_1_3y                 0.00 0.70   0.49      1.04\n",
       "exp_3p                   0.00 1.26   0.21      2.26\n",
       "age                      0.00 1.41   0.16      2.65\n",
       "bmi                      0.00 0.49   0.62      0.68\n",
       "hb_glic                  0.00 0.55   0.58      0.79\n",
       "creatinine_first         0.00 9.73 <0.005     71.94\n",
       "male                     0.00 0.90   0.37      1.44\n",
       "hypertension_baseline    0.00 1.17   0.24      2.05\n",
       "cvd_baseline             0.00 1.52   0.13      2.96\n",
       "ckd_baseline             0.00 5.71 <0.005     26.38\n",
       "ace_arb_base             0.00 0.46   0.64      0.63\n",
       "---\n",
       "Partial AIC = 12812.59\n",
       "log-likelihood ratio test = 139.00 on 12 df\n",
       "-log2(p) of ll-ratio test = 76.47"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "cuts = [365.25, 3*365.25]  # 0–1y, 1–3y, >3y\n",
    "\n",
    "cols_keep = ['patient_id','start','stop','event','statin_exp',\n",
    "             'age','male','bmi','hb_glic','creatinine_first',\n",
    "             'hypertension_baseline','cvd_baseline','ckd_baseline','ace_arb_base']\n",
    "\n",
    "subrows = []\n",
    "for _, r in tv_imp[cols_keep].iterrows():\n",
    "    segs = [r.start] + [c for c in cuts if r.start < c < r.stop] + [r.stop]\n",
    "    for k in range(len(segs)-1):\n",
    "        s, e = segs[k], segs[k+1]\n",
    "        if e <= cuts[0]:     w = '0_1y'\n",
    "        elif e <= cuts[1]:   w = '1_3y'\n",
    "        else:                w = '3p'\n",
    "        sub = r.copy()\n",
    "        sub['start'], sub['stop'] = s, e\n",
    "        sub['event'] = int((k == len(segs)-2) and (r.event == 1))  # evento fica no último subintervalo\n",
    "        sub['exp_0_1y'] = int((w=='0_1y') and (r.statin_exp==1))\n",
    "        sub['exp_1_3y'] = int((w=='1_3y') and (r.statin_exp==1))\n",
    "        sub['exp_3p']   = int((w=='3p')   and (r.statin_exp==1))\n",
    "        subrows.append(sub)\n",
    "\n",
    "tv_pw = pd.DataFrame(subrows)\n",
    "tv_pw = tv_pw[tv_pw['stop'] > tv_pw['start']].copy()\n",
    "\n",
    "# padronização robusta nas contínuas (mesmo esquema que você usou)\n",
    "def robust_scale(df, cols):\n",
    "    df = df.copy()\n",
    "    for c in cols:\n",
    "        x = df[c].astype(float).to_numpy()\n",
    "        lo, hi = np.nanpercentile(x, [0.5, 99.5]); x = np.clip(x, lo, hi)\n",
    "        med = np.nanmedian(x); mad = np.nanmedian(np.abs(x - med))\n",
    "        df[c] = (x - med) / (1.4826 * mad) if (mad and not np.isnan(mad)) else (x - med)\n",
    "    return df\n",
    "\n",
    "cont_covs = ['age','bmi','hb_glic','creatinine_first']\n",
    "bin_covs  = ['male','hypertension_baseline','cvd_baseline','ckd_baseline','ace_arb_base']\n",
    "for c in bin_covs + ['event','exp_0_1y','exp_1_3y','exp_3p']:\n",
    "    tv_pw[c] = tv_pw[c].astype(int)\n",
    "tv_pw = robust_scale(tv_pw, cont_covs)\n",
    "\n",
    "covars_pw = ['exp_0_1y','exp_1_3y','exp_3p'] + cont_covs + bin_covs\n",
    "\n",
    "ctv_pw = CoxTimeVaryingFitter(penalizer=0.5)\n",
    "ctv_pw.fit(tv_pw[['patient_id','start','stop','event'] + covars_pw],\n",
    "           id_col='patient_id', start_col='start', stop_col='stop', event_col='event',\n",
    "           show_progress=False)\n",
    "ctv_pw.print_summary()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "1285fcac-e98f-4c5b-bfd7-3da4c49a96f5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 700x450 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Black-and-white forest plot for the piecewise Cox results\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "labels = [\"Statin exposure, 0–1 year\", \"Statin exposure, 1–3 years\", \"Statin exposure, >3 years\"]\n",
    "hr     = np.array([1.02, 1.01, 1.03])\n",
    "lo     = np.array([0.99, 0.98, 0.99])\n",
    "hi     = np.array([1.06, 1.05, 1.07])\n",
    "\n",
    "y = np.arange(len(labels))[::-1]\n",
    "xerr = np.vstack([hr - lo, hi - hr])\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(7, 4.5))\n",
    "\n",
    "# Black points and error bars\n",
    "ax.errorbar(hr, y, xerr=xerr, fmt='o', color='black', ecolor='black', capsize=3, linewidth=1.5)\n",
    "\n",
    "# Reference line at HR=1 (black dashed)\n",
    "ax.axvline(1.0, linestyle='--', color='black', linewidth=1)\n",
    "\n",
    "ax.set_yticks(y)\n",
    "ax.set_yticklabels(labels)\n",
    "ax.set_xlabel(\"Hazard Ratio\")\n",
    "ax.set_title(\"Piecewise time-varying effect of statins on dialysis initiation\")\n",
    "\n",
    "xmin = max(0.95, (lo.min() - 0.03))\n",
    "xmax = hi.max() + 0.03\n",
    "ax.set_xlim(xmin, xmax)\n",
    "\n",
    "ax.invert_yaxis()\n",
    "ax.grid(False)\n",
    "\n",
    "plt.tight_layout()\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "e21a1b96-6c9a-4c63-8a2f-4278f9f61803",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 700x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "km0, km1 = KaplanMeierFitter(), KaplanMeierFitter()\n",
    "d0 = tv[tv.statin_exp == 0]\n",
    "d1 = tv[tv.statin_exp == 1]\n",
    "\n",
    "km0.fit(durations=d0['stop'], event_observed=d0['event'], entry=d0['start'], label='Unexposed')\n",
    "km1.fit(durations=d1['stop'], event_observed=d1['event'], entry=d1['start'], label='Exposed')\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(7,5))\n",
    "# Preto e branco: mesma cor (preto), estilos diferentes\n",
    "km0.plot_survival_function(ax=ax, ci_show=False, color='black', linestyle='-')\n",
    "km1.plot_survival_function(ax=ax, ci_show=False, color='black', linestyle='--')\n",
    "\n",
    "ax.set_xlabel(\"Days since T2DM diagnosis\")\n",
    "ax.set_ylabel(\"Dialysis-free survival\")\n",
    "ax.legend(frameon=False)\n",
    "ax.grid(False)\n",
    "plt.tight_layout()\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "01a877ff-75b8-4ff0-9dae-476504fe1952",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Grouping: Ever-exposed to statin during follow-up vs never-exposed\n",
      "Overall N = 36246 | No statin = 25474 | Statin = 10772\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Variable</th>\n",
       "      <th>Overall</th>\n",
       "      <th>No statin</th>\n",
       "      <th>Statin</th>\n",
       "      <th>p-value</th>\n",
       "      <th>SMD</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>age</td>\n",
       "      <td>64.00 [53.00; 74.00]</td>\n",
       "      <td>61.00 [50.00; 71.00]</td>\n",
       "      <td>71.00 [62.00; 78.00]</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.694273</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>bmi</td>\n",
       "      <td>28.03 [25.04; 31.59]</td>\n",
       "      <td>28.03 [25.00; 31.63]</td>\n",
       "      <td>28.03 [25.14; 31.48]</td>\n",
       "      <td>9.210202e-01</td>\n",
       "      <td>0.023522</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>hb_glic</td>\n",
       "      <td>6.00 [5.60; 6.80]</td>\n",
       "      <td>5.90 [5.50; 6.70]</td>\n",
       "      <td>6.10 [5.70; 6.90]</td>\n",
       "      <td>1.477707e-45</td>\n",
       "      <td>0.118549</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>creatinine_first</td>\n",
       "      <td>0.91 [0.75; 1.12]</td>\n",
       "      <td>0.88 [0.73; 1.07]</td>\n",
       "      <td>0.97 [0.80; 1.21]</td>\n",
       "      <td>3.772984e-144</td>\n",
       "      <td>0.212610</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>hypertension_baseline</td>\n",
       "      <td>22633/36246 (62.4%)</td>\n",
       "      <td>14350/25474 (56.3%)</td>\n",
       "      <td>8283/10772 (76.9%)</td>\n",
       "      <td>9.175040e-299</td>\n",
       "      <td>0.446752</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>cvd_baseline</td>\n",
       "      <td>6093/36246 (16.8%)</td>\n",
       "      <td>2224/25474 (8.7%)</td>\n",
       "      <td>3869/10772 (35.9%)</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.690711</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>ckd_baseline</td>\n",
       "      <td>908/36246 (2.5%)</td>\n",
       "      <td>380/25474 (1.5%)</td>\n",
       "      <td>528/10772 (4.9%)</td>\n",
       "      <td>2.285787e-80</td>\n",
       "      <td>0.194758</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>ace_arb_base</td>\n",
       "      <td>2296/36246 (6.3%)</td>\n",
       "      <td>937/25474 (3.7%)</td>\n",
       "      <td>1359/10772 (12.6%)</td>\n",
       "      <td>1.146872e-223</td>\n",
       "      <td>0.331173</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>male</td>\n",
       "      <td>20556/36246 (56.7%)</td>\n",
       "      <td>13649/25474 (53.6%)</td>\n",
       "      <td>6907/10772 (64.1%)</td>\n",
       "      <td>1.752349e-76</td>\n",
       "      <td>0.215417</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                Variable               Overall             No statin  \\\n",
       "0                    age  64.00 [53.00; 74.00]  61.00 [50.00; 71.00]   \n",
       "1                    bmi  28.03 [25.04; 31.59]  28.03 [25.00; 31.63]   \n",
       "2                hb_glic     6.00 [5.60; 6.80]     5.90 [5.50; 6.70]   \n",
       "3       creatinine_first     0.91 [0.75; 1.12]     0.88 [0.73; 1.07]   \n",
       "4  hypertension_baseline   22633/36246 (62.4%)   14350/25474 (56.3%)   \n",
       "5           cvd_baseline    6093/36246 (16.8%)     2224/25474 (8.7%)   \n",
       "6           ckd_baseline      908/36246 (2.5%)      380/25474 (1.5%)   \n",
       "7           ace_arb_base     2296/36246 (6.3%)      937/25474 (3.7%)   \n",
       "8                   male   20556/36246 (56.7%)   13649/25474 (53.6%)   \n",
       "\n",
       "                 Statin        p-value       SMD  \n",
       "0  71.00 [62.00; 78.00]   0.000000e+00  0.694273  \n",
       "1  28.03 [25.14; 31.48]   9.210202e-01  0.023522  \n",
       "2     6.10 [5.70; 6.90]   1.477707e-45  0.118549  \n",
       "3     0.97 [0.80; 1.21]  3.772984e-144  0.212610  \n",
       "4    8283/10772 (76.9%)  9.175040e-299  0.446752  \n",
       "5    3869/10772 (35.9%)   0.000000e+00  0.690711  \n",
       "6      528/10772 (4.9%)   2.285787e-80  0.194758  \n",
       "7    1359/10772 (12.6%)  1.146872e-223  0.331173  \n",
       "8    6907/10772 (64.1%)   1.752349e-76  0.215417  "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# ===================== CONFIG =====================\n",
    "# Define group (string): \"ever\", \"baseline\", \"initiator_vs_never\"\n",
    "GROUPING = \"ever\"\n",
    "\n",
    "# Continuous variables\n",
    "cont_vars = ['age', 'bmi', 'hb_glic', 'creatinine_first']\n",
    "# binary of baseline (1 se *_days <= 0)\n",
    "bin_from_days = {\n",
    "    'hypertension_baseline': 'hypertension_days',\n",
    "    'cvd_baseline': 'cvd_days',\n",
    "    'ckd_baseline': 'ckd_no_dialysis',\n",
    "    'ace_arb_base': 'ace_arb_days',\n",
    "}\n",
    "# binary 0/1 \n",
    "bin_vars_explicit = ['male']  # ajuste conforme sua base\n",
    "# ==================================================\n",
    "\n",
    "d0 = df.copy()\n",
    "\n",
    "# 1) Coerce numeric\n",
    "num_cols = list(set(cont_vars + list(bin_from_days.values()) + ['statin_days']))\n",
    "for c in num_cols:\n",
    "    if c in d0.columns:\n",
    "        d0[c] = pd.to_numeric(d0[c], errors='coerce')\n",
    "\n",
    "# 2) Flags of baseline from *_days (1 se <=0)\n",
    "for new_col, day_col in bin_from_days.items():\n",
    "    if new_col not in d0.columns:\n",
    "        d0[new_col] = np.where(np.isfinite(d0[day_col]) & (d0[day_col] <= 0), 1, 0).astype(int)\n",
    "\n",
    "# 3) Garantee binaries as int\n",
    "for c in bin_vars_explicit:\n",
    "    if c in d0.columns:\n",
    "        d0[c] = pd.to_numeric(d0[c], errors='coerce').round().fillna(0).astype(int)\n",
    "\n",
    "# 4) Define groups \"statin\" vs \"no statin\"\n",
    "if GROUPING == \"ever\":\n",
    "    # Any statin_days not-null -> exposition any moment\n",
    "    d0['grp_statin'] = np.where(d0['statin_days'].notna(), 1, 0).astype(int)\n",
    "    group_note = \"Ever-exposed to statin during follow-up vs never-exposed\"\n",
    "elif GROUPING == \"baseline\":\n",
    "    # Exposition at baseline: statin_days <= 0\n",
    "    d0['grp_statin'] = np.where(d0['statin_days'].notna() & (d0['statin_days'] <= 0), 1, 0).astype(int)\n",
    "    group_note = \"Statin at baseline (statin_days ≤ 0) vs no statin at baseline\"\n",
    "elif GROUPING == \"initiator_vs_never\":\n",
    "    # Start post-baseline (>0) vs never; exclude prevalent users\n",
    "    keep = d0['statin_days'].isna() | (d0['statin_days'] > 0)\n",
    "    d0 = d0.loc[keep].copy()\n",
    "    d0['grp_statin'] = np.where(d0['statin_days'] > 0, 1, 0).astype(int)\n",
    "    group_note = \"Initiators after baseline (statin_days > 0) vs never-users (prevalent users excluded)\"\n",
    "else:\n",
    "    raise ValueError(\"GROUPING must be one of: 'ever', 'baseline', 'initiator_vs_never'\")\n",
    "\n",
    "# 5) Helpers\n",
    "def fmt_median_iqr(x: pd.Series):\n",
    "    x = pd.to_numeric(x, errors='coerce')\n",
    "    med = np.nanmedian(x)\n",
    "    q1, q3 = np.nanpercentile(x.dropna(), [25, 75]) if x.notna().sum() else (np.nan, np.nan)\n",
    "    return f\"{med:.2f} [{q1:.2f}; {q3:.2f}]\"\n",
    "\n",
    "def mann_whitney_p(x, g):\n",
    "    a = x[g==0].dropna(); b = x[g==1].dropna()\n",
    "    if len(a) > 0 and len(b) > 0:\n",
    "        stat, p = stats.mannwhitneyu(a, b, alternative='two-sided')\n",
    "        return p\n",
    "    return np.nan\n",
    "\n",
    "def smd_cont(x, g):\n",
    "    a = x[g==0].dropna(); b = x[g==1].dropna()\n",
    "    if len(a) > 1 and len(b) > 1:\n",
    "        m0, m1 = a.mean(), b.mean()\n",
    "        s0, s1 = a.std(ddof=1), b.std(ddof=1)\n",
    "        sp = np.sqrt(((len(a)-1)*s0**2 + (len(b)-1)*s1**2) / (len(a)+len(b)-2)) if (len(a)+len(b)-2)>0 else np.nan\n",
    "        return np.nan if sp==0 else (m1 - m0)/sp\n",
    "    return np.nan\n",
    "\n",
    "def prop_str(x):\n",
    "    n = int(x.sum()); N = int(x.count())\n",
    "    p = 100 * (n / N) if N else np.nan\n",
    "    return f\"{n}/{N} ({p:.1f}%)\"\n",
    "\n",
    "def prop_val(x):\n",
    "    n = int(x.sum()); N = int(x.count())\n",
    "    return (n, N)\n",
    "\n",
    "def pval_binary(x, g):\n",
    "    # Tabela 2x2 para valor=1\n",
    "    a1 = int(x[(g==1)==True].sum()); a0 = int(x[(g==0)==True].sum())\n",
    "    n1 = int(x[g==1].count());       n0 = int(x[g==0].count())\n",
    "    table = np.array([[a1, n1-a1],[a0, n0-a0]])\n",
    "    # regra simples: se todas as esperadas >=5 -> qui2; senão Fisher\n",
    "    chi2_ok = (table.sum(axis=1)[:,None] * table.sum(axis=0)[None,:] / table.sum()).min() >= 5\n",
    "    if chi2_ok:\n",
    "        chi2, p, dof, _ = stats.chi2_contingency(table, correction=False)\n",
    "        return p\n",
    "    else:\n",
    "        try:\n",
    "            _, p = stats.fisher_exact(table)\n",
    "            return p\n",
    "        except Exception:\n",
    "            return np.nan\n",
    "\n",
    "def smd_binary(x, g):\n",
    "    p1 = x[g==1].mean()\n",
    "    p0 = x[g==0].mean()\n",
    "    p  = (p1*(1-p1) + p0*(1-p0))/2\n",
    "    return np.nan if p==0 else (p1 - p0)/np.sqrt(p)\n",
    "\n",
    "# 6) Construir Tabela 1\n",
    "rows = []\n",
    "g = d0['grp_statin'].astype(int)\n",
    "n0, n1 = (g==0).sum(), (g==1).sum()\n",
    "overall_N = len(d0)\n",
    "\n",
    "# Contínuas\n",
    "for v in cont_vars:\n",
    "    if v not in d0.columns: continue\n",
    "    overall = fmt_median_iqr(d0[v])\n",
    "    grp0    = fmt_median_iqr(d0.loc[g==0, v])\n",
    "    grp1    = fmt_median_iqr(d0.loc[g==1, v])\n",
    "    pval    = mann_whitney_p(d0[v], g)\n",
    "    smd     = smd_cont(d0[v], g)\n",
    "    rows.append([v, overall, grp0, grp1, pval, smd])\n",
    "\n",
    "# Binárias (de baseline + explícitas)\n",
    "bin_vars = list(bin_from_days.keys()) + bin_vars_explicit\n",
    "for v in bin_vars:\n",
    "    if v not in d0.columns: continue\n",
    "    overall = prop_str(d0[v])\n",
    "    grp0    = prop_str(d0.loc[g==0, v])\n",
    "    grp1    = prop_str(d0.loc[g==1, v])\n",
    "    pval    = pval_binary(d0[v], g)\n",
    "    smd     = smd_binary(d0[v], g)\n",
    "    rows.append([v, overall, grp0, grp1, pval, smd])\n",
    "\n",
    "tab1 = pd.DataFrame(rows, columns=[\n",
    "    'Variable', 'Overall', 'No statin', 'Statin', 'p-value', 'SMD'\n",
    "])\n",
    "\n",
    "# Ordenar por tipo (contínuas primeiro) só para estética\n",
    "order_map = {v: i for i, v in enumerate(cont_vars + bin_vars)}\n",
    "tab1['__ord'] = tab1['Variable'].map(order_map).fillna(999)\n",
    "tab1 = tab1.sort_values('__ord').drop(columns='__ord')\n",
    "\n",
    "# Info do tamanho dos grupos\n",
    "print(f\"Grouping: {group_note}\")\n",
    "print(f\"Overall N = {overall_N} | No statin = {n0} | Statin = {n1}\")\n",
    "tab1\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "3be0b238-396c-42fa-82a3-30832f1991d0",
   "metadata": {},
   "outputs": [],
   "source": [
    "tab1['p-value'] = tab1['p-value'].round(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "100e3797-7d33-4641-be7d-e7d950e6b839",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Variable</th>\n",
       "      <th>Overall</th>\n",
       "      <th>No statin</th>\n",
       "      <th>Statin</th>\n",
       "      <th>p-value</th>\n",
       "      <th>SMD</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>age</td>\n",
       "      <td>64.00 [53.00; 74.00]</td>\n",
       "      <td>61.00 [50.00; 71.00]</td>\n",
       "      <td>71.00 [62.00; 78.00]</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.694273</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>bmi</td>\n",
       "      <td>28.03 [25.04; 31.59]</td>\n",
       "      <td>28.03 [25.00; 31.63]</td>\n",
       "      <td>28.03 [25.14; 31.48]</td>\n",
       "      <td>0.92102</td>\n",
       "      <td>0.023522</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>hb_glic</td>\n",
       "      <td>6.00 [5.60; 6.80]</td>\n",
       "      <td>5.90 [5.50; 6.70]</td>\n",
       "      <td>6.10 [5.70; 6.90]</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.118549</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>creatinine_first</td>\n",
       "      <td>0.91 [0.75; 1.12]</td>\n",
       "      <td>0.88 [0.73; 1.07]</td>\n",
       "      <td>0.97 [0.80; 1.21]</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.212610</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>hypertension_baseline</td>\n",
       "      <td>22633/36246 (62.4%)</td>\n",
       "      <td>14350/25474 (56.3%)</td>\n",
       "      <td>8283/10772 (76.9%)</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.446752</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>cvd_baseline</td>\n",
       "      <td>6093/36246 (16.8%)</td>\n",
       "      <td>2224/25474 (8.7%)</td>\n",
       "      <td>3869/10772 (35.9%)</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.690711</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>ckd_baseline</td>\n",
       "      <td>908/36246 (2.5%)</td>\n",
       "      <td>380/25474 (1.5%)</td>\n",
       "      <td>528/10772 (4.9%)</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.194758</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>ace_arb_base</td>\n",
       "      <td>2296/36246 (6.3%)</td>\n",
       "      <td>937/25474 (3.7%)</td>\n",
       "      <td>1359/10772 (12.6%)</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.331173</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>male</td>\n",
       "      <td>20556/36246 (56.7%)</td>\n",
       "      <td>13649/25474 (53.6%)</td>\n",
       "      <td>6907/10772 (64.1%)</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.215417</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                Variable               Overall             No statin  \\\n",
       "0                    age  64.00 [53.00; 74.00]  61.00 [50.00; 71.00]   \n",
       "1                    bmi  28.03 [25.04; 31.59]  28.03 [25.00; 31.63]   \n",
       "2                hb_glic     6.00 [5.60; 6.80]     5.90 [5.50; 6.70]   \n",
       "3       creatinine_first     0.91 [0.75; 1.12]     0.88 [0.73; 1.07]   \n",
       "4  hypertension_baseline   22633/36246 (62.4%)   14350/25474 (56.3%)   \n",
       "5           cvd_baseline    6093/36246 (16.8%)     2224/25474 (8.7%)   \n",
       "6           ckd_baseline      908/36246 (2.5%)      380/25474 (1.5%)   \n",
       "7           ace_arb_base     2296/36246 (6.3%)      937/25474 (3.7%)   \n",
       "8                   male   20556/36246 (56.7%)   13649/25474 (53.6%)   \n",
       "\n",
       "                 Statin  p-value       SMD  \n",
       "0  71.00 [62.00; 78.00]  0.00000  0.694273  \n",
       "1  28.03 [25.14; 31.48]  0.92102  0.023522  \n",
       "2     6.10 [5.70; 6.90]  0.00000  0.118549  \n",
       "3     0.97 [0.80; 1.21]  0.00000  0.212610  \n",
       "4    8283/10772 (76.9%)  0.00000  0.446752  \n",
       "5    3869/10772 (35.9%)  0.00000  0.690711  \n",
       "6      528/10772 (4.9%)  0.00000  0.194758  \n",
       "7    1359/10772 (12.6%)  0.00000  0.331173  \n",
       "8    6907/10772 (64.1%)  0.00000  0.215417  "
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tab1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "36789256-9c11-401f-8f20-7599edc2b464",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "from pathlib import Path\n",
    "\n",
    "# ================== CONFIG ==================\n",
    "OUTDIR = Path(\"time_summaries\")\n",
    "OUTDIR.mkdir(exist_ok=True)\n",
    "\n",
    "# identificar variáveis de tempo\n",
    "days_cols = [c for c in df.columns if c.endswith(\"_days\") and c != \"index_day\"]\n",
    "# se quiser focar em um subconjunto, defina manualmente:\n",
    "# days_cols = [\"statin_days\",\"has_days\",\"dlp_days\",\"dcv_days\",\"drc_conservador_days\",\n",
    "#              \"ultima_visita_days\",\"dialysis_days\",\"death_days\",\"bmi_days\",\"hb_glic_days\"]\n",
    "\n",
    "# definição de grupos (ever statin vs never)\n",
    "df[\"_ever_statin\"] = np.where(df[\"statin_days\"].notna(), 1, 0).astype(int)\n",
    "\n",
    "# ============================================\n",
    "\n",
    "def _median_iqr(x):\n",
    "    x = pd.to_numeric(x, errors=\"coerce\")\n",
    "    x = x.dropna()\n",
    "    if x.empty:\n",
    "        return (np.nan, np.nan, np.nan)\n",
    "    med = float(np.median(x))\n",
    "    q1, q3 = np.percentile(x, [25, 75])\n",
    "    return med, float(q1), float(q3)\n",
    "\n",
    "def _format_days_years(m, q1, q3):\n",
    "    if np.isnan(m):\n",
    "        return (\"NA\", \"NA\")\n",
    "    # em dias\n",
    "    s_days = f\"{m:.1f} [{q1:.1f}; {q3:.1f}]\"\n",
    "    # em anos\n",
    "    m_y, q1_y, q3_y = m/365.25, q1/365.25, q3/365.25\n",
    "    s_years = f\"{m_y:.2f} [{q1_y:.2f}; {q3_y:.2f}]\"\n",
    "    return s_days, s_years\n",
    "\n",
    "def summarize_times(df, cols):\n",
    "    rows = []\n",
    "    N = len(df)\n",
    "    for c in cols:\n",
    "        x = pd.to_numeric(df[c], errors=\"coerce\")\n",
    "        obs = x.notna().sum()\n",
    "        miss = N - obs\n",
    "        m, q1, q3 = _median_iqr(x)\n",
    "        s_days, s_years = _format_days_years(m, q1, q3)\n",
    "        n_le0 = int((x <= 0).sum(skipna=True))\n",
    "        n_eq0 = int((x == 0).sum(skipna=True))\n",
    "        n_gt0 = int((x > 0).sum(skipna=True))\n",
    "        # proporções entre os que ocorreram (obs>0)\n",
    "        p_le0 = (n_le0/obs*100) if obs else np.nan\n",
    "        p_eq0 = (n_eq0/obs*100) if obs else np.nan\n",
    "        p_gt0 = (n_gt0/obs*100) if obs else np.nan\n",
    "        rows.append({\n",
    "            \"variable\": c,\n",
    "            \"N\": N,\n",
    "            \"observed\": obs,\n",
    "            \"missing\": miss,\n",
    "            \"observed_%\": round(obs/N*100, 1),\n",
    "            \"median_days [Q1;Q3]\": s_days,\n",
    "            \"median_years [Q1;Q3]\": s_years,\n",
    "            \"min_days\": None if obs==0 else float(np.nanmin(pd.to_numeric(x, errors=\"coerce\"))),\n",
    "            \"max_days\": None if obs==0 else float(np.nanmax(pd.to_numeric(x, errors=\"coerce\"))),\n",
    "            \"≤0 n (%) among observed\": f\"{n_le0} ({p_le0:.1f}%)\" if obs else \"NA\",\n",
    "            \"=0 n (%) among observed\": f\"{n_eq0} ({p_eq0:.1f}%)\" if obs else \"NA\",\n",
    "            \">0 n (%) among observed\": f\"{n_gt0} ({p_gt0:.1f}%)\" if obs else \"NA\",\n",
    "        })\n",
    "    out = pd.DataFrame(rows)\n",
    "    # ordenar por fração observada (só estética)\n",
    "    return out.sort_values(\"observed_%\", ascending=False).reset_index(drop=True)\n",
    "\n",
    "# ---- 1) Sumário geral de tempos ----\n",
    "time_summary = summarize_times(df, days_cols)\n",
    "time_summary.to_csv(OUTDIR/\"time_summary_all.csv\", index=False)\n",
    "# Se quiser LaTeX direto:\n",
    "# with open(OUTDIR/\"time_summary_all.tex\",\"w\") as f:\n",
    "#     f.write(time_summary.to_latex(index=False, escape=False, longtable=True))\n",
    "\n",
    "# ---- 2) Sumário por grupo (ever statin vs never) ----\n",
    "def summarize_by_group(df, cols, group_col=\"_ever_statin\"):\n",
    "    res = []\n",
    "    for c in cols:\n",
    "        for gval, gname in [(0, \"No statin (ever=0)\"), (1, \"Statin (ever=1)\")]:\n",
    "            dfg = df[df[group_col]==gval]\n",
    "            s = summarize_times(dfg, [c]).iloc[0].to_dict()\n",
    "            s[\"group\"] = gname\n",
    "            res.append(s)\n",
    "    out = pd.DataFrame(res)\n",
    "    return out[[\"group\",\"variable\",\"N\",\"observed\",\"missing\",\"observed_%\",\n",
    "                \"median_days [Q1;Q3]\",\"median_years [Q1;Q3]\",\n",
    "                \"min_days\",\"max_days\",\n",
    "                \"≤0 n (%) among observed\",\"=0 n (%) among observed\",\">0 n (%) among observed\"]]\n",
    "\n",
    "time_summary_groups = summarize_by_group(df, days_cols)\n",
    "time_summary_groups.to_csv(OUTDIR/\"time_summary_by_group.csv\", index=False)\n",
    "\n",
    "# ---- 3) Histogramas PB por variável ----\n",
    "def bw_histograms(df, cols, bins=60, winsor=(1,99)):\n",
    "    for c in cols:\n",
    "        x = pd.to_numeric(df[c], errors=\"coerce\").dropna()\n",
    "        if x.empty:\n",
    "            continue\n",
    "        # limitar eixos aos percentis para tirar cauda extrema\n",
    "        lo, hi = np.percentile(x, [winsor[0], winsor[1]])\n",
    "        fig, ax = plt.subplots(figsize=(6,4))\n",
    "        ax.hist(x, bins=bins, range=(lo, hi), histtype='step', linewidth=1.5)  # preto e branco (contorno)\n",
    "        ax.axvline(0.0, linestyle='--', linewidth=1.0)  # linha vertical no index (diagnóstico)\n",
    "        ax.set_title(f\"{c}: distribution of days relative to T2DM diagnosis\")\n",
    "        ax.set_xlabel(\"Days since T2DM diagnosis (index = 0)\")\n",
    "        ax.set_ylabel(\"Count\")\n",
    "        ax.grid(False)\n",
    "        fig.tight_layout()\n",
    "        fig.savefig(OUTDIR/f\"hist_{c}.png\", dpi=300, bbox_inches=\"tight\")\n",
    "        plt.close(fig)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "a227b61e-02bf-40d2-bb0f-562df3b8c723",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Arquivos gerados em: /home/jovyan/work/nefro_statin/time_summaries\n"
     ]
    }
   ],
   "source": [
    "bw_histograms(df, days_cols)\n",
    "\n",
    "print(\"Arquivos gerados em:\", OUTDIR.resolve())\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "b6881487-7af8-4403-bab4-f2b2e4226c1a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>variable</th>\n",
       "      <th>observed</th>\n",
       "      <th>missing</th>\n",
       "      <th>observed_%</th>\n",
       "      <th>median_days [Q1;Q3]</th>\n",
       "      <th>≤0 n (%) among observed</th>\n",
       "      <th>=0 n (%) among observed</th>\n",
       "      <th>&gt;0 n (%) among observed</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>hypertension_days</td>\n",
       "      <td>25597</td>\n",
       "      <td>10649</td>\n",
       "      <td>70.6</td>\n",
       "      <td>0.0 [-9.6; 0.0]</td>\n",
       "      <td>22633 (88.4%)</td>\n",
       "      <td>15801 (61.7%)</td>\n",
       "      <td>2964 (11.6%)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>statin_days</td>\n",
       "      <td>10772</td>\n",
       "      <td>25474</td>\n",
       "      <td>29.7</td>\n",
       "      <td>1.3 [0.3; 476.8]</td>\n",
       "      <td>2004 (18.6%)</td>\n",
       "      <td>0 (0.0%)</td>\n",
       "      <td>8768 (81.4%)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>cvd_days</td>\n",
       "      <td>10610</td>\n",
       "      <td>25636</td>\n",
       "      <td>29.3</td>\n",
       "      <td>0.0 [0.0; 551.8]</td>\n",
       "      <td>6093 (57.4%)</td>\n",
       "      <td>3730 (35.2%)</td>\n",
       "      <td>4517 (42.6%)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ace_arb_days</td>\n",
       "      <td>10601</td>\n",
       "      <td>25645</td>\n",
       "      <td>29.2</td>\n",
       "      <td>1.2 [0.2; 349.7]</td>\n",
       "      <td>2296 (21.7%)</td>\n",
       "      <td>0 (0.0%)</td>\n",
       "      <td>8305 (78.3%)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>death_days</td>\n",
       "      <td>1834</td>\n",
       "      <td>34412</td>\n",
       "      <td>5.1</td>\n",
       "      <td>82.4 [17.4; 296.9]</td>\n",
       "      <td>0 (0.0%)</td>\n",
       "      <td>0 (0.0%)</td>\n",
       "      <td>1834 (100.0%)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>dialysis_days</td>\n",
       "      <td>1151</td>\n",
       "      <td>35095</td>\n",
       "      <td>3.2</td>\n",
       "      <td>206.7 [7.1; 1139.4]</td>\n",
       "      <td>0 (0.0%)</td>\n",
       "      <td>0 (0.0%)</td>\n",
       "      <td>1151 (100.0%)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            variable  observed  missing  observed_%  median_days [Q1;Q3]  \\\n",
       "0  hypertension_days     25597    10649        70.6      0.0 [-9.6; 0.0]   \n",
       "1        statin_days     10772    25474        29.7     1.3 [0.3; 476.8]   \n",
       "2           cvd_days     10610    25636        29.3     0.0 [0.0; 551.8]   \n",
       "3       ace_arb_days     10601    25645        29.2     1.2 [0.2; 349.7]   \n",
       "4         death_days      1834    34412         5.1   82.4 [17.4; 296.9]   \n",
       "5      dialysis_days      1151    35095         3.2  206.7 [7.1; 1139.4]   \n",
       "\n",
       "  ≤0 n (%) among observed =0 n (%) among observed >0 n (%) among observed  \n",
       "0           22633 (88.4%)           15801 (61.7%)            2964 (11.6%)  \n",
       "1            2004 (18.6%)                0 (0.0%)            8768 (81.4%)  \n",
       "2            6093 (57.4%)            3730 (35.2%)            4517 (42.6%)  \n",
       "3            2296 (21.7%)                0 (0.0%)            8305 (78.3%)  \n",
       "4                0 (0.0%)                0 (0.0%)           1834 (100.0%)  \n",
       "5                0 (0.0%)                0 (0.0%)           1151 (100.0%)  "
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "time_summary[['variable', 'observed', 'missing', 'observed_%',\n",
    "       'median_days [Q1;Q3]', \n",
    "       '≤0 n (%) among observed', '=0 n (%) among observed',\n",
    "       '>0 n (%) among observed']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "fbce247f-55e2-48bd-98c9-7800f3ca74d9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Salvo: finegray_input.csv\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "base = df.copy()\n",
    "for c in ['dialysis_days','death_days','last_visit','statin_days']:\n",
    "    base[c] = pd.to_numeric(base[c], errors='coerce')\n",
    "\n",
    "t_dial  = base['dialysis_days'].where(base['dialysis_days'] >= 0)\n",
    "t_death = base['death_days'].where(base['death_days'] >= 0)\n",
    "t_cens  = base['last_visit'].where(base['last_visit'] >= 0)\n",
    "ftime = pd.concat([t_dial, t_death, t_cens], axis=1).min(axis=1)\n",
    "\n",
    "fstatus = np.zeros(len(base), dtype=int)\n",
    "dial  = t_dial.notna()  & ((t_death.isna()) | (t_dial <= t_death)) & ((t_cens.isna()) | (t_dial <= t_cens))\n",
    "death = t_death.notna() & ((t_dial.isna()) | (t_death <  t_dial))  & ((t_cens.isna()) | (t_death <= t_cens))\n",
    "fstatus[dial.values]  = 1  # diálise\n",
    "fstatus[death.values] = 2  # morte\n",
    "\n",
    "base['ftime'] = ftime\n",
    "base['fstatus'] = fstatus\n",
    "base['statin_baseline'] = ((base['statin_days'].notna()) & (base['statin_days'] <= 0)).astype(int)\n",
    "\n",
    "# flags basais (se não existirem)\n",
    "def flag(days):\n",
    "    d = pd.to_numeric(base[days], errors='coerce')\n",
    "    return ((d.notna()) & (d <= 0)).astype(int)\n",
    "for new, day in [('hypertension_baseline','hypertension_days'),\n",
    "                 ('cvd_baseline','cvd_days'),\n",
    "                 ('ckd_baseline','ckd_no_dialysis'),\n",
    "                 ('ace_arb_base','ace_arb_days')]:\n",
    "    if new not in base: base[new] = flag(day)\n",
    "\n",
    "covars = ['statin_baseline','age','male','bmi','hb_glic','creatinine_first',\n",
    "          'hypertension_baseline','cvd_baseline','ckd_baseline','ace_arb_base']\n",
    "\n",
    "fg_export = base.loc[base['ftime'].notna() & (base['ftime']>=0), ['ftime','fstatus']+covars].copy()\n",
    "\n",
    "# imputação simples p/ contínuas (como no seu pipeline)\n",
    "for c in ['age','bmi','hb_glic','creatinine_first']:\n",
    "    fg_export[c] = pd.to_numeric(fg_export[c], errors='coerce').fillna(fg_export[c].median())\n",
    "for c in ['statin_baseline','male','hypertension_baseline','cvd_baseline','ckd_baseline','ace_arb_base']:\n",
    "    fg_export[c] = fg_export[c].fillna(0).astype(int)\n",
    "\n",
    "fg_export.to_csv(\"finegray_input.csv\", index=False)\n",
    "print(\"Salvo: finegray_input.csv\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "8d89f3e4-0bdf-4c21-96b5-5bcd0af895da",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ok] salvo finegray_newuser_lag30_censor.csv | N = 34242\n",
      "[ok] salvo finegray_newuser_lag90_censor.csv | N = 34242\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "# ======== Parâmetros ========\n",
    "LAGS = [30, 90]  # dias de carência a testar\n",
    "\n",
    "# df: um registro por paciente (já no seu ambiente)\n",
    "# Campos esperados: 'patient_id','statin_days','dialysis_days','death_days','ultima_visita_days',\n",
    "#                   'age','male','bmi','hb_glic','creatinine_first',\n",
    "#                   'has_days','dlp_days','dcv_days','drc_conservador_days','ieca_bra_days'\n",
    "\n",
    "base0 = df.copy()\n",
    "\n",
    "# utilitário: flag basal a partir de *_days (<=0 no índice)\n",
    "def flag_baseline(s):\n",
    "    s = pd.to_numeric(s, errors='coerce')\n",
    "    return ((s.notna()) & (s <= 0)).astype(int)\n",
    "\n",
    "# construir flags basais se você ainda não tiver:\n",
    "for new, col in [('hypertension_baseline','hypertension_days'),\n",
    "                 ('cvd_baseline','cvd_days'),\n",
    "                 ('ckd_baseline','ckd_no_dialysis'),\n",
    "                 ('ace_arb_base','ace_arb_days')]:\n",
    "    if new not in base0.columns:\n",
    "        base0[new] = flag_baseline(base0[col])\n",
    "\n",
    "# manter só new-users (statin_days > 0) e nunca-usuários (NaN); excluir usuários no baseline (≤0)\n",
    "base = base0[(base0['statin_days'].isna()) | (base0['statin_days'] > 0)].copy()\n",
    "base['statin_newuser'] = (base['statin_days'] > 0).astype(int)\n",
    "\n",
    "# preparar tempos brutos (>=0 em relação ao índice)\n",
    "for c in ['dialysis_days','death_days','last_visit','statin_days']:\n",
    "    base[c] = pd.to_numeric(base[c], errors='coerce')\n",
    "\n",
    "t_dial  = base['dialysis_days'].where(base['dialysis_days'] >= 0)\n",
    "t_death = base['death_days'].where(base['death_days'] >= 0)\n",
    "t_cens  = base['last_visit'].where(base['last_visit'] >= 0)\n",
    "\n",
    "# função que aplica o L dias de lag para iniciadores e retorna ftime/fstatus prontos p/ Fine–Gray\n",
    "def build_finegray_with_lag(df_in: pd.DataFrame, lag_days: int, strategy=\"censor\"):\n",
    "    df = df_in.copy()\n",
    "    # tempo bruto do primeiro evento observado (antes de aplicar lag)\n",
    "    ftime_raw = pd.concat([t_dial, t_death, t_cens], axis=1).min(axis=1)\n",
    "\n",
    "    # status bruto: 1 = diálise, 2 = morte, 0 = censura\n",
    "    fstatus_raw = np.zeros(len(df), dtype=int)\n",
    "    cond_dial  = t_dial.notna()  & ((t_death.isna()) | (t_dial <= t_death)) & ((t_cens.isna()) | (t_dial <= t_cens))\n",
    "    cond_death = t_death.notna() & ((t_dial.isna()) | (t_death <  t_dial))  & ((t_cens.isna()) | (t_death <= t_cens))\n",
    "    fstatus_raw[cond_dial.values]  = 1\n",
    "    fstatus_raw[cond_death.values] = 2\n",
    "\n",
    "    df['ftime']   = ftime_raw\n",
    "    df['fstatus'] = fstatus_raw\n",
    "\n",
    "    # aplicar lag apenas em iniciadores\n",
    "    starters = df['statin_newuser'] == 1\n",
    "    t_lag = df.loc[starters, 'statin_days'] + lag_days\n",
    "\n",
    "    if strategy == \"censor\":\n",
    "        # censurar administrativamente qualquer evento (1 ou 2) ocorrido antes do t_lag\n",
    "        mask_early = starters.copy()\n",
    "        mask_early.loc[starters] = df.loc[starters, 'ftime'] < t_lag\n",
    "        # censurar no t_lag\n",
    "        df.loc[mask_early, 'ftime']   = t_lag[mask_early]\n",
    "        df.loc[mask_early, 'fstatus'] = 0\n",
    "\n",
    "        # se a censura administrativa ultrapassar censura natural, garanta ftime>=0 e não-NaN\n",
    "        df = df[df['ftime'].notna() & (df['ftime'] >= 0)].copy()\n",
    "\n",
    "    elif strategy == \"exclude\":\n",
    "        # excluir iniciadores que tiveram evento antes do t_lag (mais conservador)\n",
    "        mask_exclude = starters & (df['ftime'] < t_lag)\n",
    "        df = df.loc[~mask_exclude].copy()\n",
    "\n",
    "    else:\n",
    "        raise ValueError(\"strategy must be 'censor' or 'exclude'\")\n",
    "\n",
    "    # montar dataframe final com covariáveis (imputar contínuas por mediana como no seu pipeline)\n",
    "    covars = ['statin_newuser','age','male','bmi','hb_glic','creatinine_first',\n",
    "          'hypertension_baseline','cvd_baseline','ckd_baseline','ace_arb_base']\n",
    "    out = df[['ftime','fstatus'] + covars].copy()\n",
    "\n",
    "    for c in ['age','bmi','hb_glic','creatinine_first']:\n",
    "        out[c] = pd.to_numeric(out[c], errors='coerce')\n",
    "        out[c] = out[c].fillna(out[c].median())\n",
    "\n",
    "    for c in ['statin_newuser','male','hypertension_baseline','cvd_baseline','ckd_baseline','ace_arb_base']:\n",
    "        out[c] = out[c].fillna(0).astype(int)\n",
    "\n",
    "    # salvar CSV\n",
    "    fname = f\"finegray_newuser_lag{lag_days}_{strategy}.csv\"\n",
    "    out.to_csv(fname, index=False)\n",
    "    print(f\"[ok] salvo {fname} | N = {len(out)}\")\n",
    "    return out\n",
    "\n",
    "# gerar para 30 e 90 dias (estratégia padrão: censurar eventos no lag)\n",
    "for L in LAGS:\n",
    "    _ = build_finegray_with_lag(base, lag_days=L, strategy=\"censor\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "2d447010-e9c3-4f8c-958e-161f4ea5663a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Results of R"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "9d6c95d1-cca3-4154-a0d0-f82b48db7e3c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1500x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# --- data ---\n",
    "data = [\n",
    "    (\"Statin at baseline (≤0 d)\", 0.727, 0.508, 1.040, \"0.082\"),\n",
    "    (\"Age (per unit)\",            1.030, 1.021, 1.040, \"< 0.001\"),\n",
    "    (\"Male\",                      1.252, 1.044, 1.500, \"0.015\"),\n",
    "    (\"BMI (per unit)\",            1.006, 0.994, 1.010, \"0.001\"),\n",
    "    (\"HbA1c (per unit)\",          1.178, 1.113, 1.250, \"< 0.001\"),\n",
    "    (\"Creatinine (baseline)\",     1.417, 1.287, 1.560, \"< 0.001\"),\n",
    "    (\"Hypertension (baseline)\",   1.327, 1.084, 1.630, \"0.006\"),\n",
    "    (\"CVD (baseline)\",            1.819, 1.494, 2.210, \"< 0.001\"),\n",
    "    (\"CKD conservative (baseline)\", 5.480, 4.149, 7.240, \"< 0.001\"),\n",
    "    (\"ACEi/ARB (baseline)\",       1.171, 0.857, 1.600, \"0.320\"),\n",
    "]\n",
    "\n",
    "df = pd.DataFrame(data, columns=[\"Model\",\"HR\",\"LCL\",\"UCL\",\"p\"])\n",
    "df[\"logHR\"], df[\"logLCL\"], df[\"logUCL\"] = np.log(df[\"HR\"]), np.log(df[\"LCL\"]), np.log(df[\"UCL\"])\n",
    "df[\"y\"] = np.arange(len(df))[::-1]\n",
    "\n",
    "# --- FIGURE (desligue auto-layout) ---\n",
    "fig, ax = plt.subplots(figsize=(15, 6), constrained_layout=False)\n",
    "\n",
    "# --- interval of data ---\n",
    "LEFT_CORRIDOR = 0.90             # \n",
    "xmin = df[\"logLCL\"].min() - LEFT_CORRIDOR\n",
    "xmax = df[\"logUCL\"].max() + 0.20\n",
    "ax.set_xlim(xmin, xmax)\n",
    "\n",
    "# --- size ---\n",
    "# [LEFT, BOTTOM, WIDTH, HEIGHT] em fração da figura\n",
    "ax.set_position([0.28, 0.18, 0.42, 0.72])   # ↓ WIDTH menor afasta barras dos textos\n",
    "\n",
    "# --- bars/markers ---\n",
    "for _, r in df.iterrows():\n",
    "    ax.hlines(r[\"y\"], r[\"logLCL\"], r[\"logUCL\"], lw=1.5, color=\"black\")\n",
    "ax.plot(df[\"logHR\"], df[\"y\"], \"s\", ms=6, color=\"black\")\n",
    "ax.axvline(0.0, ls=\"--\", lw=1.0, color=\"black\")\n",
    "\n",
    "ax.set_xticks([ -1.0, -0.5, 0.0, 0.5, 1.0 ])\n",
    "ax.set_xlabel(\"log(HR)\")\n",
    "ax.set_ylim(-1, len(df))\n",
    "ax.set_yticks([])\n",
    "for s in [\"left\",\"right\",\"top\"]: ax.spines[s].set_visible(False)\n",
    "\n",
    "# after df[\"y\"]\n",
    "ROW_GAP = 1.0                    \n",
    "df[\"y\"] = np.arange(len(df))[::-1] * ROW_GAP\n",
    "ax.set_ylim(-0.5*ROW_GAP, (len(df)-1 + 0.5)*ROW_GAP)  \n",
    "\n",
    "# transform: x in [0..1] \n",
    "trans = ax.get_yaxis_transform()\n",
    "\n",
    "X_MODEL = -0.34    # column \"Model\" \n",
    "X_HR    = -0.02    # column \"Hazard Ratio (95% CI)\"\n",
    "X_P     =  1.06    # column \"p-value\"\n",
    "\n",
    "# head\n",
    "ax.text(X_MODEL, (len(df)-1 + 0.5)*ROW_GAP, \"Model\", transform=trans,\n",
    "        ha=\"left\", va=\"bottom\", fontsize=11, fontweight=\"bold\")\n",
    "ax.text(X_HR,    (len(df)-1 + 0.5)*ROW_GAP, \"Hazard Ratio (95% CI)\", transform=trans,\n",
    "        ha=\"left\", va=\"bottom\", fontsize=11, fontweight=\"bold\")\n",
    "ax.text(X_P,     (len(df)-1 + 0.5)*ROW_GAP, \"p-value\", transform=trans,\n",
    "        ha=\"right\", va=\"bottom\", fontsize=11, fontweight=\"bold\")\n",
    "\n",
    "# lines of tables alined to bars\n",
    "for _, r in df.iterrows():\n",
    "    ydat = r[\"y\"]                                  # <-- EM DADOS\n",
    "    ax.text(X_MODEL, ydat, r[\"Model\"], transform=trans, ha=\"left\",  va=\"center\", fontsize=10)\n",
    "    ax.text(X_HR,    ydat, f\"{r.HR:.2f} ({r.LCL:.2f}–{r.UCL:.2f})\",\n",
    "            transform=trans, ha=\"left\",  va=\"center\", fontsize=10)\n",
    "    ax.text(X_P,     ydat, r[\"p\"],       transform=trans, ha=\"right\", va=\"center\", fontsize=10)\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "fb17fed5-4bf5-4d11-9ef3-ec88d9e71b51",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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ehb+/v2rrp7Zx4fLly5kGtLNnz0br1q1RsWJFtG3bVhnDvHfvXowYMUL1+3TAgAFwdnbGxIkTja7ztGnTkJiYiJiYGOzcuRPXrl3D559/Djc3N6PLeFVOnDgB4MUDhHLlyuHSpUvKNnNzc0ydOhWjR4/W2y8+Ph5ffPEFUlNTcefOHWzZsgUPHjxQGmm0qlWrhu7du2PFihWoVKkS3nvvPTx//hzbt29HfHw81q5d+8pbQ//++2/069cPixYtUtKmT5+O8ePHY8qUKZg9e7aSPnfuXKPnBnBycsKwYcOU/1++fBmAegNX2velsXbs2IGjR48iLi4Op06dwp49e1C1alUMHDhQL+/cuXNx4sQJ/PXXXxn2In0r5XKX8Hfe+vXrBYBMnjxZRETatm0rAOTSpUs6+S5cuCAAxMfHR2d8Rma0Y5jVxm6oMTSGOTo6WkxMTGTq1Kl622bNmiUA5McffxQRkSpVqoiNjY08efJEL692DLPaeIdBgwYJAJkzZ45OekZjZjZv3iwApEuXLkad37179zLMD0D8/f2lUKFCEhQUJGPHjpXmzZsrr/39+/eVvAkJCaLRaKR27drSr18/+fjjj+WDDz4QMzMz8fT0lJiYGCVvamqqVKpUSTQajbz//vvy8ccfy+DBg6VBgwZiZ2enU4dz585JgQIFxNzcXNq2bSujRo1SxnFVrFhRddyOMWOYAUi5cuXEx8dHatSoISNGjJD3339fAEjlypX13ldffvmlABAvLy/p27evDBkyRHx9fQWAzJgxQ/X18/b2lpIlS2b4NzAGxzC/mXJ7DPPevXsznbMBOTDuMTs4hpnyGmOus9kdw5wesjiGWTsvRr169VS3b9y4UX788UeJioqShIQEuXDhggwbNkxMTEzEzc1NHjx4YHS91Mav3rx5UwBIzZo1VfcbP368AJC9e/cadZz9+/eLm5ubznWodu3acvToUb28P//8swCQX3/9VUkz5l7O1tZWZ56XmTNnGlW3rMjuGObp06cLADE1NZXAwED566+/5MmTJ7Jr1y7x9PQUALJ161a9/bT3bNofW1tbWblypeoxUlNTZcKECaLRaJT8pqamMnLkSHn06JHqPjk1hln7mt+8eVMnPT4+XpydncXNzU0n3dvb26jvKgDi7e2ts2/x4sUFgCQnJ+vV47fffhMA8tlnnxld9w8//FDneA0aNFDmMEjr8uXLYmNjI+PGjVPSMrqnf9uwhTmXLV++HADQuXNn5d9169ZhxYoVmDRpkpLv0KFDAIA6depkq2VkwYIFquMZfHx8jFp/bvv27UhNTUWzZs1Uz8HGxgatWrVSzmHEiBFYu3YtevXqZVT9Hjx4gM2bNwMAqlevrprnzz//VJ4mJyUl4fLly9iyZQsqVKiAadOmGXWcw4cPAwAqVqxoMI+xTwktLCwQGRmp15Vo//79eO+997BgwQKEhoYqZf71118YPny4zlNGQL91vHv37oiLi8PRo0dRoUIFJX3+/PlKt/3sLulw5swZjB49WqdFu0+fPliyZAkOHDiAWrVqKfnGjBmD+vXrY/Pmzco4p4SEBNSvXx8TJkxAly5d9FrRK1eujA0bNuD+/fsGZ1dMy9DYqCtXrmQ4PIDyvsaNG2e7a2Rqaio8PDwAvBhPaeyT7IsXLxqVr1OnTnrdFF+1lJSU13o8ojeZ9t7I0P2J9n5Dq2TJkpgzZw7MzMwwc+ZMLF68GB9//PErrqVxNmzYgK5du6Jjx44YP3483N3d8ffff2PIkCGoXbs2fvvtN9SpUwcAEBMTg6FDh6Jr165o2LBhlo4TGxuL1NRU3Lx5E6tXr8Ynn3yCkydPYuXKla/itLIkNTUVwIsxxRs3blRavRs1aoTvv/8ejRs3xpw5c/TuMfPnzw8RwfPnz3H9+nV8//336NatG86dO4epU6cq+bRd97dt24alS5eiWbNmEBFs3rwZQ4cOxe7du3H06FGjZjXPLm9vb+V7S8va2hoVKlTA3r17ce/ePRQoUADA/8Zc5wULFizAggUL8OjRIxw5cgSjRo1ClSpVsHv3bvj6+gJ40Xurb9++8PDwwGeffZbLNc4luRywv9Oio6PF3Nxc56nrs2fPxMnJSby9vXVa/LSzKWY0A7WazGbJTt+abKiFuU2bNuLl5aWXfvLkSQEgHTp0UNJu3bolJiYmUrt2bb382qdRTZs2ldDQUAkNDZX+/ftLwYIFBYD069dPbx+1WbK1P87OzjJjxgzVJ21qFi5cKABkxYoVqtuRxaeEhpQrV07ndTx9+rQAkE8++STD/Y4dO2bw6W1KSooULFhQKlWqpKRltYXZzs5Ob2bwffv2CQCZP3++kqZ94qj2NHvbtm16+bUGDhwoAOT06dMZnqeWtoUj/Y+lpSVbmN9AaVtR+WP4hy3MlBfk1RbmpKQkyZ8/v9jb2+t9X2Xm7NmzAsDomYJhoHXx6dOnArzocaZm8ODBAkBOnDiRYfn37t0Te3t7CQgI0OvFdfPmTbGwsNBpxe7evbu4uLjotfBltbegiMi0adMEgKxdu9bofTKT3RbmBQsWCACpU6eO3raUlBSxtLQUR0dHo8rq16+fANBpnf/2228FgCxYsEAvv7a33OLFi/W25WQLc2BgoOq2jh07CgC5fPnySx1Dq3LlygJAp8ej1rp16wSAzJo1K9vlR0VFibm5uTRt2lRJ++GHHwTQ71HBFmZ6LX766SckJycrrcvAi6dvbdq0wZIlS5Q1dnPC+fPnsz0RVHJyMn777Td07dpVb5t2wrK05+Du7o733nsPe/fuxdWrV1G0aFG9/bZv347t27frpPXs2VOnVTe9MWPGYMaMGQBePE28fv06FixYgLFjx+Lo0aM6a+wZ8uDBAwDIcNKfrDwlvHDhAqZOnYp9+/bhzp07SE5OVvYpUaKE8nvp0qVRunRpTJs2DWfOnEHz5s0RHByM4sWL6xzn6NGjAF60sKqtC2lmZmZ0K5qa4sWL682KqT3XtONpjh49CjMzM/zyyy96Zdy7dw+Aemue9nU1dkz52bNnVdNzYlZOyl21atXKscl3jHXx4kXcuXMn03xubm650sL8559/vtZjEr2Jtm/fjpiYGPTq1SvLK1loe9K97NqwdnZ2cHV1xbVr15CamqrXy+XKlSsAoLTAGXLw4EE8ffpUtXegh4cHfH19cerUKSXt9OnTuH//vnKPkZ52cquTJ0/q9EBTo53jZf/+/Wjbtm2GeV817f2Q2hwsJiYmsLe3N3p953r16uG7777Dn3/+iYCAAAD/m6gqKChIL782Le3r/Cpo743Si46OBgCdMdQvM4bZ19cXJ06cwJUrV/QmEjP2fZkRLy8v+Pr6KpOLAS/elwBQt25d1X0GDhyIgQMHYs6cOTp1fZswYM5Fy5cvh6mpKTp06KCT3qVLFyxZsgTLly9XAmbt8gy3bt167fXct28fnj59qtdVJjk5GT///DPy5cuHxo0b62zr0qUL9uzZgxUrVqgGfgsXLsSAAQPw/PlznD9/HkOGDMHSpUtRsWJFDBkyJNM6mZqaokiRIpg1axaOHz+ODRs24MCBA6hZs2aG+1lbWwN4MaO4IYa+qLTT9z958gQFChTAhQsXULVqVSQnJ6NRo0bw9fWFra0tNBoNli1bhsTERGVfMzMzhIeHY8KECVi/fj22bt0KAPDz88OMGTPQsmVLAC9mgASAdevWYd26dZm+DlmlNumFduKMtF1GHz58iOfPn2c44YjaDUlCQgKA/73O9O7atWsXbG1ts7WviChLgOTPn9/oYSjh4eEGv9DTWrVq1WubHVYrLi4OdnZ2r/WYRG8ibXfskJCQLO97/PhxAFCWG3oZtWvXxrp163D06FEEBgYq6QkJCThw4ADKlSuX6URS2klYDS1pdP/+fZ1uwq1bt1aW3Ezrjz/+wKVLl9C2bVs4OjoaNeTp9u3bAKAzOVZuCQwMhKWlJc6fP6+3LSYmBjExMUYHeWrnldHrrE17ld2xASAqKgq3bt3SaXBJSEjAqVOn4OrqqnNvOXfuXERFRRlVrre3t04QWrt2baxevRp79uxRHhho7d69G6ampi818aqI4M6dOzqvb/Xq1VVX4Ll06RL++OMP1KxZE35+fihbtmy2j5vn5XYT97tK20U3ox87Ozul615uTvr10Ucf6S0SLyKyadOmTM+hSJEiOvU11H3jyZMn4u7uLlZWVhIVFaWzLbMuQCNHjhRAf7IwNT/++KMAkEWLFqluByDFihVT3Va3bl0BINHR0SLyv+7HBw4c0Mvr5+enN1GDVkpKipw4cUImT54szs7OYmZmJqdOnRIRkXnz5gkAg5NapJfVLtlq3e3V8lesWFEsLS0lJSXFqHpo9ejRI0e6HnHSrzdTbk/6lZqaKsHBwRlek4KDg7N0Dc0pnPSL8pqc6JKdlJQk58+f1/veTg9Gdsm+d++emJubS9GiRQ1+ThMTE+XcuXN66f/995+ULFlSAMju3bszPZa2Xoa6427fvl0ASMOGDeX58+dK+sSJEwWAzJs3T6/u58+f15lgKioqSkxMTMTW1lbvPmzRokUCQFq1apVpPQ3dy12/fl11gqaHDx9KhQoVBIDs2rVLtazsTH5oTJfsW7duyfnz5/W602vvD9Ler6Smpkr//v31yvz333/l6dOnemVfv35dPD09xcTEROc9MGXKFAEgjRs3lqSkJCX92bNnynfCpk2b9MrLyS7ZgP6wQm23+OHDh79U+WlFR0eLra2tuLu7y927d5X0iIgI0Wg0esMR4uLi5Pz583Lr1i2ddLXhBKmpqRIWFiYApGPHjpnWhV2y6ZXTdmVu3LixzlpyWufOncOhQ4ewfv16dO/eHSVLlkS1atVw5MgRrF27Fu3btzdYdlJSUo4+Sdu+fTvq1aunt8i59ilwu3btVJ+yHjlyBP/88w/279+vTGhhiL29PSZPnow+ffpg0qRJ+P77742un7ZVVjupREa0XX3TLmmQnrFPCa9evQoXFxe9J3l3797FlStX9Lp1a5mYmKBSpUqoVKkSfHx80K1bN+zatQvly5dXnhYeOXJEdamJ1yUgIAAnT57EqVOnsrQG5L///gt7e3udtSaJXheNRoMNGzYYXFpKuw4zl5QiMmzGjBm4cOECACj/9u3bVxliMXPmTKXr882bN1GqVCkEBQXpfebST9h15coVJS1//vyYOXOm3rFXrVqF5ORkdO/e3eDnND4+HmXKlEG1atVQqlQpFCxYENevX8fWrVsRGxuLwYMHG700ZUaaNGmC9u3bY82aNahWrRrq16+P8+fPY8uWLQgMDMSAAQN08i9YsAATJ07E0qVLlfP08vLC0KFDMWfOHFSqVAlt2rSBu7s7zpw5g19//RX29vaYMmVKtut44sQJdOzYEbVr10axYsXg5OSEGzduYNu2bXjy5Al69OiBRo0a6ewjIgCMb3m+cOGCMhxOO+Rl+/btyu+1atVCnz59lPyG1nmeMWMGIiIi0KtXL2zatAm+vr44ePAgDh06hNKlS2P8+PFK3u3bt+PTTz9FnTp1UKRIEdjY2ODq1avYtm0bEhMTERYWpnRPB4BBgwbhhx9+wK5du1C2bFk0atQIqamp2LVrF65cuYL69eujefPmWXhls87f3x/bt29HcHAwatSogX/++Qdbt26Ft7c3JkyYkGPHKVCgAL788ksMGjQIlSpVQvv27fHkyRP8/PPPcHFxwaxZs3TyHz16FO+99x5CQkKU2AN40bXd09MT5cuXh6enJx4+fIgDBw7g7Nmz8PLy0lvu9J2X2xH7uyg5OVkKFixocOklEZG///5bAEjdunWVtMOHD4u5ubk4OjrqPTEUefEk7YsvvtBZ+ullW5jPnTun2iIbExMjFhYW4uXlZfAp8NatWwWA9OrVS0nL6GlUUlKSeHt7i7m5uURGRirpGT3RvHHjhuTLl08AyOHDhzM9v+fPn4u9vb0EBwerbkcWnhL26dNHNBqNzlPOxMREZWmwtC3M165dU30Cr12SS9s6npqaKhUqVBBzc3PVp7+PHz+WkydPKv9/VS3MJ06cEBMTEwkMDNRZHkvr3LlzOk82RV68r62traVhw4Z6+bOKLcxvptxuYdZKTU2V8PBw6d27tzRv3lx69+4t4eHhudKyrMUWZsprDF1nta3Khn6uXbum5NV+f6h9t2RUhqEeWJUrVxaNRqNzjPSePXsmH330kQQEBEj+/PnFzMxMnJ2dpX79+lme4AqZtC4mJSXJ1KlTxdfXVywsLMTT01OGDx+u2voZGhqq930s8uJ69MMPP0iNGjXE3t5ezMzMxMPDQ7p27SoXL140qp6G7uWioqJk6NChUqFCBaXHWv78+aVhw4byyy+/qJZVuXJlKVSokE5LbEYymnhV7fXLqAX7zp070rdvX3F3dxdzc3Px8vKS4cOHy8OHD3XynTlzRvr37y+lS5cWR0dHMTMzE3d3d2nZsqXOcltp3bt3T4YNG6b8rSwtLcXf31+mTp0qiYmJqvvkZAtzUFCQXLlyRZo3by729vbi4OAg7dq1k+vXr79U2YasXbtWAgICxNraWvLlyydt27ZV7d2n/fulP8c5c+ZIgwYNxMPDQ8zNzcXW1lbKly8vEyZMMHpZtnephZkBcy7YsmWLAJDOnTtnmK9ixYqi0Wh0Aq2NGzcqa+1VrVpVhg4dKuPGjZMuXbpIgQIFBIB89dVXSn7thevDDz9UZqVO/3P79m0lf/qA+YsvvhAA8t9//+nUbf78+QJAxo8fb7D+2gcDaWe6zOzDpZ3FesCAAUqa9sNes2ZNpc4TJkyQkJAQcXBwEADSrVu3DF/LtDp06CA2NjaqN62A7jrM48aNU9Zh9vb21pmV8MiRI2JiYiL58uWTAQMGyODBg8XPz0+KFCki5cuX17kh2Lhxo5iamkqtWrWkb9++MnbsWGXNZldXV53g89y5c+Lq6ioajUYaNGggw4cPl8GDB0uTJk3ExsZG+vfvr/fa5HTALCIyc+ZM0Wg04uzsLJ07d5YxY8ZIjx49pEqVKgJADh06pJP/jz/+0Hv/ZRcD5ncbg0uiV4/X2cwD5rdNbGysmJmZyYwZM3K7KnlCTgfM9PZiwJwL2rRpozquJL25c+cKAJk8ebJO+n///SejRo0Sf39/sbOzEwsLC/H29pbOnTvrTfme2bJSAHRaLNMHzLVr15aKFSvq1U07rf2FCxcyPIdhw4YJAPnxxx9FJPOA+dmzZ1KoUCGxtLRUgnRDTzft7e0lMDBQFi1alKWxtr///rsAkJ9//llvW1afEu7cuVMCAgLExsZGChQoIF27dpVbt25JUFCQTsB848YN+fjjj6VKlSri4uIiVlZW4uvrK4MHD5YbN27olXv79m0ZOnSoFCtWTCwsLMTZ2VnKly8vo0aN0mnRfpUBs4jI3r17pVmzZpI/f34xNzcXT09PqVevnnz99dd6gczAgQPFysrK6CeTGeGN3LuNATPRq8frrG4reJkyZXK7Oq/cnj17xNbWVq9F912zcePGDFvJs4oB89tPI/L/gxmI0nn48CEKFCiA8ePHY9KkSbldnRwjIvD390ehQoWUpQjo5cTHx8Pb2xstW7bM0vhzQ7RjzQ0tO0Vvt7QzSsfGxmZ7tm0iMozXWeis4lGwYEEMGjQo9ypDr82FCxd0ls2sUKECWrVqle3yNBqN6jh+entw0i8yaOfOnUhJSdFbTupNp9FoMG3aNLRs2RLHjx9XXcKBsmbRokWIjY1FaGhobleFiIjIKGrLXtLbz8/Pj397yhIGzGRQ586d0blz59yuxivRokULzJo1y+DaiJQ1tra2WLZsGQoXLpzbVaG3gJmZmbIGa15YQ5SIiMgQdtZ9+7FLNhHlOewqSET0avE6S0RkHJPcrgARERERERFRXsS+bkRElKeICOLj4wEANjY20Gg0uVwjIiIielexhZmIiPKU+Ph42NnZwc7OTgmciYiIiHIDA2YiIiIiIiIiFQyYiYiIiIiIiFQwYCYiIiIiIiJSwYCZiIiIiIiISAUDZiIiIiIiIiIVDJiJiIiIiIiIVHAdZiIiylNMTU3Rtm1b5XciIiKi3MKAmYiI8hQrKyusXbs2t6tBRERExC7ZRERERERERGoYMBMRERERERGpYMBMRER5SlxcHDQaDTQaDeLi4nK7OkRERPQOY8BMREREREREpIIBMxEREREREZEKBsxEREREREREKhgwExEREREREalgwExERERERESkggEzERERERERkQqz3K4AERFRWqampmjSpInyOxEREVFuYcBMRER5ipWVFbZv357b1SAiIiJil2wiIiIiIiIiNQyYiYiIiIiIiFQwYCYiojwlLi4Otra2sLW1RVxcXG5Xh4iIiN5hHMNMRER5Tnx8fG5XgYiIiIgtzERERERERERqGDATERERERERqWDATERERERERKSCATMRERERERGRCgbMRERERERERCo4SzYREeUpJiYmCAoKUn4nIiIiyi0MmImIKE+xtrZGREREbleDiIiIiF2yiYiIiIiIiNQwYCYiIiIiIiJSwYCZiIjylLi4OBQoUAAFChRAXFxcbleHiIiI3mEcw0xERHlOTExMbleBiIiIiC3MRERERERERGoYMBMRERERERGpYMBMREREREREpIIBMxEREREREZEKBsxEREREREREKjhLNhER5SkmJiaoUqWK8jsRERFRbmHATEREeYq1tTWOHTuW29UgIiIiYpdsIiIiIiIiIjUMmImIiIiIiIhUMGAmIqI8JT4+Hj4+PvDx8UF8fHxuV4eIiIjeYQyYiYgoTxERREVFISoqCiKS29UhoreURqNRfsqWLZvb1aHXZNOmTTp/+x49euR2lSiPY8BMRERERAB0g8j0P7t27TKqjJMnT2L8+PEICAiAi4sLrKysUKpUKXz66aeIi4vTy79//34MGzYMFSpUgJOTE2xsbFCxYkXMmjULycnJevnXrl2LDz74AEWLFoWdnR3y5cuHSpUqYdasWVnulVK+fHmEhoZi0KBBGea7ceMGHBwcoNFoMHbs2CwdIy4uDh9//DG8vb1hZWUFX19fTJkyRe/ckpOTsW7dOnTt2hUlS5aEjY0NnJycULduXWzatCnDY2zbtg0NGzaEi4sLrK2tUbx4cfTo0QNPnz7NUl0NOXv2LLp06QIPDw9YWlrC09MTrVq1wunTpzPdNyIiIsP3lUajweTJk5X8y5YtyzT/jz/+qOQPCwvLNP/+/fuV/H5+fggNDcXQoUNz5LV50zx//hzTp09HiRIlYGVlBS8vL4wcORKxsbFZKicmJgb9+/eHu7s7rKysULZsWXzzzTcGH3RHRkaiU6dOyJ8/P2xsbBAQEIDVq1cbLP/06dNo1qwZ8uXLBzs7O9SpUwe7d+9WzTt//nx069YNfn5+MDExgUajwbNnz7J0PhnRCB/f02sSHByMyMhIREZG5nZVAAAdOnTAhQsXcOrUKWg0GgBAjx49sHz5cly7dg0+Pj65W0EjabuuRkREKGmv+7XesmULWrVqhRMnTqBixYovXV6ZMmUAvPiCpndPXFwc7OzsAACxsbGwtbXN5RoRvX0MXWc1Gg28vb1VW926du0KX1/fTMsODAzEsWPHUKNGDWWJuN9++w3nzp1D+fLl8eeffyqfcQBwc3PDgwcPEBwcjPLlyyMhIQHbtm1DVFQUGjRogF27duksMdepUyecOnUKVapUgYeHBxISErB//34l7cCBA7CwsMi0nhqNBiEhIVi2bFmmeZs0aYI//vgDcXFxGDNmDGbMmJHpPsCLILhevXrYv38/3nvvPVStWhVHjhxBREQE2rRpg3Xr1il5L1y4gFKlSsHJyQn169eHr68vbt++jXXr1iEuLg6TJ0/GhAkT9I7x8ccf48svv0SJEiXQsGFD2NjY4MaNG9izZw9OnDgBT09Po+pqyKZNm9ChQwc4ODigefPmcHNzw927d3HgwAFMmDABXbt2zXD/yMhIg6/xt99+i7t37+LgwYOoXr06AODUqVMGHxB8/vnnSEpKQlRUlHJeEREROvdAWs+ePcPnn38OR0dH3LlzB1ZWVnr1KlKkiNHvgbdFx44dsXr1alSuXBn16tXDhQsXsGXLFlSvXh0RERFGfXYePXqEwMBAXLx4ES1atICfn5/yfhsxYgRmzZqlkz8qKgpVq1bF/fv30b59e3h6emLz5s34999/MX/+fAwZMkQn/4kTJ1CnTh2ICDp37gwHBwesWbMGt2/fxoYNG9CyZUud/Nr7eG9vbzx+/BiPHj1CQkKC3t882ySHpKamyrp166RFixbi5uYm5ubm4ujoKNWrV5fp06fL/fv3lbzXrl0TANKhQwe9cp49eyZNmzYVANK1a1d5/vy5iIh4e3sLAOXHyspKXF1dJSgoSCZMmCBXrlzJqVOhbNL+XUNCQlS3BwUFibe392utkyHHjx8XjUYjq1ev1kkPCQkRAHLt2rXcqVg2eHt7S1BQkE7a636tU1NTxd/fXxo1apQj5ZUuXVpKly6dI2XRmyc2Nla51sfGxuZ2degt8La/p7JzfoauswD0vlOyasGCBXrfo8nJydK6dWsBINOnT9fZ9sUXX8jdu3d10uLj46VatWoCQFatWqWzLSEhQfW4PXr0EACyYsUKo+qZ0T1LWitWrBBTU1P58ssvBYCMGTPGqPJFRL7++msBIL1799ZJ79WrlwCQzZs3K2n//feffPvtt3rnd/nyZcmXL5+YmZnJjRs3dLatWrVKAMjQoUMlJSVFZ1tKSopeWlZdvnxZbGxspGbNmvLo0SO97cnJydku++bNm2JqaiolSpQwKv/hw4cFgDRo0MCo/L/88osAkL59+6puz+y+9U0XHx8vZ86c0Unbvn27AJCGDRsqMZaIyMSJEwWAzJs3z6iyR48eLQBk0qRJSlpycrI0aNBANBqNnDp1Sid/u3bt9D6bsbGx4u/vLzY2NnL79m2d/FWrVhUTExOJiIhQ0u7evStubm7i4eGh9xnZsWOH3Lt3T0Re3AMDMHidyI4cCZifPHkijRs3FgDi5OQkHTt2lLFjx8pHH30k1apVE41GI/ny5VPyGwqY4+LipEGDBsqbO+2H3NvbW2xtbSU0NFRCQ0Nl3Lhx0rt3bylTpowAEDMzM5kyZUpOnA5lU2YXnqioKLl8+fLrrZQBLVu2FE9PT70vkrclYM6N13rJkiUCQI4cOfLSZTFgfre97cENvX6v8z2Vmpoqe/fuld69e0vz5s2ld+/esnfvXklNTX1lx8xrAbMhhw4dEgDStGlTo/Jrg8EPP/zQqPybN28WABIWFmZUfmOCpTt37oizs7MMHz5cwsPDsxwwV6lSRTQajdy8eVMn/b///hONRiMtWrQwqpz+/fsLAFm7dq2SlpqaKr6+vuLr66sT/GTk1q1bcv78eYmLizMqf69evcTExOSVNEx9/vnnAkCmTp1qVP6BAwcKAPnpp5+Myv/+++8LADlw4IDq9pwKmLWfmStXrkjz5s3FwcFBHBwcpF27dnoPOF6HQ4cOSb9+/cTBwUHv3Nq2bSsA5NChQzrp8fHx4uDgIOXKlcu0/JSUFClYsKA4OjrqBaUHDhwQAPLRRx8paQ8fPhQzMzMpWbKkXlk//fSTAJDZs2craf/8848AUG2EmTp1qgCQDRs2GKzfqwiYzXKilbpr167YtWsX2rRpgyVLlsDR0VFn+8mTJ9G/f/8My4iNjUXTpk3xxx9/YOjQoZgzZ47SvK5lZ2eHsLAwvX13796Nbt26YcKECXBwcNBr1qe8wcvLK7erAAC4desWtm3bhqFDh+p08Xqb5MZr3bp1awwcOBDff/89qlat+tqPT0SU2x4+fIjWrVvrdQ9dsmQJgoODsWHDBuTLly93KpcFDx8+xLfffosHDx7A3d0d9evXR+HChV+6XHNzcwCAmZlxt59Zzf/rr78C+F9385wwePBg2NvbY/LkyTh27FiW9o2Pj8eJEydQqlQpeHh46GwrVKgQ/Pz8dMbWZkTttTh16hQuX76MkSNHIjk5GRs2bMDly5eRP39+NGrUSPVeYNy4cVi+fDnCw8MRHByc4TFFBBs2bEDFihVRtGhRhIeH49ixY7C0tEStWrVQuXJlo+puyPLly2FiYoJu3bplmjcxMRG//PILHBwc8MEHH2Sa/86dO/jtt99QvHhx1KhR46XqaYwHDx6gTp068PX1xaBBg3D27FmsXbsWx44dw4kTJ+Ds7PxKj3/79m38+OOPWLp0KS5cuACNRoM6deqgU6dOOvn2798POzs7vfs0a2tr1KxZEzt37sSTJ0/g4OBg8Fj//vsvoqOj0aRJE70uz4GBgbCzs9N5Xx8+fBjPnz9HvXr19MqqX7++Uq/hw4crvwMwmP+TTz7B/v37jXof5JiXjbh37twpAMTf318SExMN5ku7LX0L86NHjyQwMFAAyLhx41T39/b2FldXV4PlHzx4UABIvnz55MmTJ0bVPS4uTiZPnixly5YVa2tryZcvnwQGBsqCBQt08j179kwmTZokJUuWFEtLS8mfP7+0a9dOzp8/r1pPb29vefjwofTu3VtcXFzE2tpa6tevr5r/4MGD0rRpU3F3dxcLCwtxc3OT+vXry9atW/Xybt++XerWrSuOjo5iZWUllStXlpUrV+rl07aSXrp0SSZNmiRFixYVU1NTWbp0qdSqVUusra1VX6Pnz5+Lq6urFC9eXEk7c+aMDBkyRMqUKSP29vZiY2MjVapUkWXLlunsu3TpUp0u82l/tNJ3E+7atatSTzWVK1cWe3t7nSdEcXFxMnHiRClVqpRYWlqKs7OztG3bVi5evKhahhptl6o///wzw9fu008/lcKFC4ulpaVUrFhRNm3apJf/zz//lN69e0uJEiXExsZG7O3tpU6dOrJt2zbVY69cuVKqVasm+fLlE2tra/Hy8pL27dvLuXPndPIlJyfLvHnzpEKFCmJtbS0ODg7SuHFj1dZbY7tka8/typUrMm3aNPHx8RELCwvx9/dXfb+JiFy/fl369Okjnp6eYmFhIYULF5Zhw4apdssSEWnUqJHY29tLUlKS6nZjsYX53RYXF6e8B4xtBSHKyOtoYU5NTZXg4GCD34UAJDg4+JW0NOd0C3P6HzMzMxk9evRL1/2jjz7KUrfPFi1a6HVbTuunn36S0NBQGT58uFSvXl0ASPPmzY3uhoxMWhfXr18vAGTHjh0iIlluYf7777+VOqlp1qyZANAZtqgmNjZW3N3dxdLSUqKjo5X0xYsXCwAZPXq0FC9eXOdvZm5uLl988YVeWdp7gfDw8Ezrf+nSJQEgLVu2VIZLpv1p3769PHv2LNNy1Bw7dkwASP369Y3Kv3btWgEgffr0MSq/9l4vo96nOdnCDED69eunkz5t2jQBIMOHD9dJnzNnjtJjNrOfOXPmGDxuYmKirFu3Tpo2bSqmpqYCQIoWLSphYWGqPSWfPn2qxGxqBg8eLADkxIkTGZ7v1q1bBYAMGTJEdXvZsmXF3t5e+f9XX30lAGTWrFmq+e3s7HTqNHLkSAEg69ev18sbExOT4WdKJI92ye7YsaMAkB9++MHofdIGzPfv35fKlSvr9YNPL7OAWUSUL6qNGzdmWoenT59KpUqVBIBUqlRJRo0aJYMHD5Y6depIkSJFlHwpKSlKN/HKlSvLmDFjpEuXLmJubi4ODg5y8uRJvXq6u7tLlSpVpHz58jJs2DBp166daDQa8fT01PkiO3bsmJibm4uzs7P06tVLxo0bJz179pRSpUpJ//79dcrVfvC9vLykb9++MmTIEPH19RUAMmPGDJ282othw4YNxc3NTXr37i3Dhg2THTt2KGNpfvzxR73X5LfffhMA8tlnnylpY8aMEVdXV+nQoYOMHj1aBg4cqHrckydPytChQwWAlC9fXufDrpU+iNOOo5g8ebJeXf79918BIN27d1fS4uLipGrVqkrXlxEjRkjXrl3F0tJSnJycVB9IqGnatKmYmZlJfHy83jbta/f++++Lh4eHDBs2TAYNGiT58uUTjUYj69at08nfoUMH8fHxkS5dusiYMWOkX79+4u7uLhqNRm/M1bx58wSAFC9eXIYMGSIff/yxdOrUSQoWLKiTNyUlRblJqFKligwdOlR69eolTk5OYmlpKfv27dMpN6sBc4sWLcTDw0P69esn/fv3FwcHBzE1NdW7QJ47d04KFCgg5ubm0rZtWxk1apQy9KJixYqqF6LQ0FDVrj5ZxYCZiHLS6wiY9+7dm2GwrP0xJlDJqpwMmEePHi3Hjh2TR48eSUxMjGzdulVKliwpAGTatGnZrmNERISYmZmJr6+v6vdveitXrhQAUqtWLYOBevogrkuXLln6+2YULD148EDc3NykU6dOSlpWA2Zt99QuXbqobu/cubMAkKtXr2ZYTp8+fQSAfPLJJzrp06dPFwBiamoqgYGB8tdff8mTJ09k165d4unpKQAMPhA3hrYLvampqbi7u8u2bdvkyZMncvr0aaldu7YAkFGjRmWrbG1wpnY/qkb7cGH//v1G5S9btqyYmJhIVFSUwTw5GTCbmZnpdbuPj48XZ2dncXNz00lPPy9TRj9q89GcPHlSPvroI3FxcREAYm9vLz179pR9+/Zl+FDr5s2bAkBq1qypun38+PECQPbu3Zvh+Wq7Uad/P2rVqFFDACgPrrTdqBcvXqya38PDQ7y8vJT/9+3bVwDI77//rpc3KSlJAEidOnUM1i9PBszaP3pmH/a0tG/QunXrSrly5TJ86pD2OJkFzJ9++qkAkAkTJmRahyFDhhj8oP/333/K79qnd23bttV5E+7YsUMASNWqVfXqCUA6duyoM55k8uTJeheG4cOHCwA5ffq0Xh1iYmKU30+fPi0mJiZSv359ndaW+Ph4qVGjht4kENrAqGjRonLnzh2dcqOjo8XMzEyaNGmid8yePXsKAJ3A88aNG3oTOjx//lzef/99sbOz0/liyuqkX0lJSeLi4qL6ha2dfGDnzp1K2qhRowSAfPfddzp5jx8/Lubm5kZPOOXi4iJly5ZV3aZ97QoVKqRMHiDy4imrjY2NuLm56fSWiIyM1Ls4xcXFSYUKFXQevIiIVKxYUQoVKqR3o5CcnKzTYrtgwQLlQpS27MjISHFycpJSpUrppGc1YPbz89N5f+3fv18A/QlJqlSpIjY2NnoPhbSBf/pJW0REtmzZYtTnWUt7w5b+x9LSkgEzEeWYtAFlrVq1JCgoKMd/3NzcjLoBdnNzy/Fj16pVK8cCZjU3btwQJycncXBwyFYPovPnz0v+/PnF3t5e/vrrr0zz//HHH2JtbS0eHh5y/fr1TPNHR0fL2rVrpXDhwlK+fHmd7++MZHTP0qNHD3F2dtaZjCw3AmZtg8l7772n99prAxG1SZN27dql3Gtnl7b+gH4r/71795Seh1ltZU5MTBQXFxext7c3qhfR3bt3lYctxjhx4oQAkHr16mWYLycD5mLFiqluq1u3rgDQ6RnwMiIjI5W/SbVq1WTFihVG98RiwJx9Lx0wW1lZCYAsfVi0b1DtjzFvVGMC5oULFwoAGTBgQIb5kpKSxM7OTnWWtfSCgoJEo9Godht+7733BIBOd2BtwJz+Ah8VFSUAZMSIEUqaNmA21CVZ68MPP9QLZLW2bdsmAGT+/PlKmjYw+uabb1TLa9y4sZibm+sETc+ePRNHR0epWLFihnXR2rBhg96T8uzMkj1gwADVhwZ+fn5SoEABJVh//vy5ODo6SrVq1VTLbtu2rWg0GoNdhbUSEhIy/ALRvnaff/653jbtZBO7du3K8BgiIrNnzxZAd/KwihUrSpEiRTIcuiAiUqZMGfHw8FCdwEP70CDtrIdZDZjVZg8tUqSIVKpUSfm/tquU2k2BdrKHtPm1jhw5IoB+9yNDGDAT0euQNmB+239eRcAs8r9ZbtUe8mfkypUr4uHhITY2Nno9pNQcPXpUHBwcpECBAnrDlTKjHZ5n7CRhhu5ZtL0F0veefN1dsrX3tYGBgfL06VO97doH7GrBQ0pKilhaWoqjo6NRdVWjnXzJ1NRU9UGJtgdm+gfrmdHeQ/bq1cuo/Np7qox6oqal7fqf2WzpORkwBwYGqm7T9sTNqYlYo6OjxcfHRwCItbW1dO7cWX799VejhiGwS3b25cikX9lVoUIFREVFYeXKlWjWrBnatm2bI+WmnywsvYsXLyI2Nhbvv/9+putznTlzBm5ubqrrDgYFBSE8PBynT59GiRIllPR8+fLpTY6hnezh0aNHSlqbNm0wd+5cVKtWDV27dkX9+vVRp04dvUnTjh49CjMzM/zyyy96dbh3755yTulVqlRJ9Zw6deqEXbt2Yd26dcpkbDt37sTjx4/1JgdISUnBokWLsGLFCpw7dw6xsbE6C5Lfvn1b9RjG6tSpE7799lusWrUK5cqVA/BiEosLFy5g0KBByuQWFy9exOPHj5GSkqI68dutW7cgIrh06ZKy5qOaBw8eAECmk66oTRBRvXp1LFy4EGfOnEGjRo0AvFjjb/bs2Vi7di0uXbqEuLg4nX1u376trOfcrl07jB8/HuXKlUOnTp1Qt25dVKtWTWe9u7i4OJw7dw7FihXD5MmT9epw7tw55fXw9/fP8BwMKV++vF6ah4eHzt/y6NGjAIArV66ovt5mZmaq7znt63r//n2j6mJoneWcnLCF3jzx8fEICAgAABw7dgw2Nja5XCN6m9SqVQumpqY5Xu7Fixdx586dTPO5ubmhZMmSOXrslJQU/PnnnzlaZnr58+cHAL3vuYxERUXhvffew/3797F161bUqVMnw/wnT55Eo0aNYGpqit9//x2lSpXKUh0DAwNhY2Nj9ERahpw+fRoA0KtXL/Tq1Utv++eff47PP/8cQ4cOxdy5cw2WU7RoUWg0Gly5ckV1+5UrV5AvXz7VCaGWLl2KQYMGoWLFiti5c6fOutVa2nvP9PeNAGBiYgJ7e3s8efLEYP0yU6xYMZiYmMDGxkaZdCwt7XETEhKyVO7y5csBQHWtb0P5NRoNunfvnmne5ORk/Pzzz7C3t0ebNm2yVK+Xob0fTy86OhoAdCbRmjt3rk48kBEnJycMGzZM+X+BAgVw9epVhIeHY+nSpdiwYQN+/vlneHh4oGvXrggJCUHp0qVVy7Kzs4OrqyuuXbuG1NRUvYlvte/TzNZa125Xe1+npqYiMjISxYsXNyp/dHQ0YmNjdY6ZUX5j65jTXjpgdnV1RVRUFG7duoUiRYpkad+SJUti8eLFqF+/Pjp37gxzc3O9haizQnvDr72oG/L48WMA0JuxUM2TJ0/g7e2tus3V1VXJk5bazHLawC8lJUVJq1mzJn7//XdMnToVX3/9NebPnw8zMzM0b94c8+fPVxZkf/jwIZ4/f46JEycarKfaF1jBggVV837wwQfo378/fvnlFyVg/uWXX6DRaNCxY0edvAMHDsTixYvh5+eHTp06oUCBAjAzM0NkZCSWL1+OxMREg3UyRu3atVG4cGGsXr0a06dPV+oCAJ07d1byPXz4EABw/PhxHD9+3GB5mX2RW1tbA3gR6GakQIECemna1zPt37tly5b47bffULlyZYSEhMDZ2RmmpqY4deoUNm/erPP6jB07Fo6Ojli4cCHCwsIQFhYGBwcH9OnTB9OmTYOlpSUePXoEEcHly5ez/Pc2lqH3Z9r3pvb1XrduHdatW2d02dovTe3rTJQdIqI8HEr7gI4oJ+zatQu2trY5Xm54eDjq1q2bab5Vq1ZlOjtxVsXFxakGVDlJ+91r6J4ovVu3bqFevXq4desW1q9fjwYNGmSY/+zZs2jYsCGeP3+O3bt3qz7czczjx48RHx9v9KzahpQtWxa9e/fWS799+zZ27NiBcuXKISAgANWrV8+wHBsbG1SuXBknTpzArVu3dO47b968iQsXLqB58+Z6+61atQp9+vRB6dKl8dtvv8HJyUm1/MDAQFhaWuL8+fN622JiYhATE/NSgYWVlRUCAwNx8OBB3Lx5E4UKFdLZrj1uVlbmiImJwY4dO1C0aFHUqlUr0/ynT5/G6dOn8d577xn13tuxYwdiYmLQs2fP1/qwVRsLpf0bJyQk4NSpU3B1ddW5r5w7dy6ioqKMKtfb21snYAZeNAzWrVsXdevWxddff43Vq1dj6dKl+OKLL/DFF1+gSpUqCAkJQadOneDi4qKzb+3atbFu3TocPXoUgYGBOnU9cOAAypUrl+EM2cCLBzUFCxbEgQMH8OzZM53Gx8OHDyM2Nha1a9dW0gIDA2FmZoY9e/bolbV7926lXmnrCAB79uzB6NGjM83/WrxsE7W2q8HSpUuN3if9LNmHDh0Se3t7sbCwkO3bt6vuk5VJv9RmM05L20WmXbt2mdY1X7584u7urrotLCxMAMiaNWt06qk2QF8k47Eyjx8/lm3btkmnTp0EgAQEBCjbKlasKJaWlkbP+mjMWsJt27YVExMTuXnzpsTGxoqNjY3Url1bJ8/t27dFo9FI48aN9Y6tXQw+7d89O12yRf7XzfjQoUOSmpqqvIZpx+mePn1aAONnRzQkNTVVzMzMpHr16qrbta+d2qQSK1asEADKrJPa7sd9+/bVyztjxgy9Lutp3bx5U1asWKHMDj969GgRefE+AIyfNVIk612y1d4X6fNrxymrzcKekYiICAGMm0cgI5z0693GdZgpp3GWbH1q19mzZ8+qDhuaOXOmAC/Gf6eVlJQk58+f15tYKTo6WkqVKiWmpqayevXqTOvy77//ipubm9jY2Mgff/yRYd7ExET5559/9NKTk5Old+/eAkDGjh2b6TFFjFuHOa2MumQ/evRIzp8/rzd+WjvZavp5Qnr16iWA/tjgjRs3ipmZmRQvXlxvXLKaHj166N2PpaamKus2p69rVmbJFhFZtmyZAJCePXvqvHd//vlnAaA3VC6zdZ619xcTJ0406vja4YvLly83Kv8HH3wgAIzq/p9bs2S/KhcuXJAxY8aIh4eHAC9mSk8/34x2wt2GDRvqDP3Tzh2Ufgb7e/fuyfnz5/WGPI4ePVqvm3xycrI0bNhQNBqNXjd97ZCOtN3kY2Njxd/fX3UMfkBAgJiYmEhERISSdvfuXXFzc8t0SG2eHMOsXVaqXLlyGU4EkdGyUiIvJh6ytbUVS0tL+e233/T2N3ZZKWdnZ9VxHmllZQxzcHCwaDQa1bEH9erVE0B/DHN2Ama1crUTdvXr18+oMQVaxgTM2vEjs2fPVgbvL1y4UCePdnbEr776Sm9/7aRpaS/Q2nHa3bp1Uz2moYD5r7/+EuDFIufaCSbSX+C1f7Py5cu/9M1GuXLlpECBAqrbjBnDrJ2IbNWqVQKoz0DZvHlzo76Q4uPjxc7OTvz8/JS0EiVKSMGCBY3+oL+KgFn7eTI0PsWQ7777TgAYdYOUEQbM7zYGzJTTXtd76sGDBwaD5uDgYHnw4MErOW5OBcxDhw6V/PnzS+vWrWXYsGEyZMgQqVKligBQHVOsvZ9L/x1Up04dAV6sLqK2VE76RhYvLy8BXkzSpJY/7eonDx8+FI1GIwEBARISEiJjx46V3r17K+M6y5cvn+l8Jlo5GTBrl9dMuzqIyIv7F+2kbO+9956MGTNGmQOndevWOnnPnz8vFhYWSoCt9lqkD0Tu3LkjPj4+otFopGXLljJy5Ehlia3SpUvL48ePdfJ3797dYMOAmpSUFGU28mrVqsnIkSOldevWYmJiIvb29nr1ySwgr1Spkmg0mgzvUbWSk5OlYMGCepPMGhITEyMWFhZStGhRo+4VczJg9vf3l0KFCklQUJCMGzdOuQ/09vbOdNmwnPb8+XPZvn27tG3bVi+IFxFp37698vkcM2aMsjJLYGCg3gMz7eon6T+zDx8+lBIlSgjwYtmxMWPGKKsepZ2vSSsyMlIKFiwopqam0rlzZxk9erSyf9p5mLSOHz8uNjY2YmNjI71795bhw4dLoUKFRKPRqDaMTp8+XUJCQiQkJERcXV0FgHTt2lVJM3YiQENeOmAW+d86ee3bt9f7YIq8WMs37WzSagGzyItJFqytrcXa2lpvhraMAubdu3crs1N+/fXXRtU5o1my004L//333yt1Tfvh+/XXXwVQnyXb2ID50KFDem/M5ORkqVixopiYmCgfsBMnToiJiYkEBgbqTNSlde7cOZ1ZHI0JmLWTfAUEBEizZs3EzMxM783033//CfBixu+0Dh06JJaWlnofoCdPnghgeEItQwGzyItJvtzc3JSg9NSpU3p5tMtWTZs2Te9CmJycrLqushrtMSIjI/W2ZTZLtqurq/I3+/PPP1WfZGvXbUz/haH2tPP27dtiYWEh5cuXV9LmzJkjAGTQoEF6M5Snpqa+9LJSxgTMqampUqFCBTE3N1f90nv8+LHqJB/apS9u3bqlty0rGDC/2xgwU057ne+p1NRUCQ8Pl969e0vz5s2ld+/eEh4e/kpalrVyKmDeuXOnfPDBB+Lj4yM2NjZiaWkpJUqUkKFDh6pe1w0FzJktm5M+f0Z50987JSUlSVhYmNSuXVtcXV3FzMxMHBwcpGrVqvL5559nqVXpdQTMIi/+PqNHj5bChQsrAd2kSZP0Gpq05Wf0o9aj886dO9K3b19xd3cXc3Nz8fLykuHDh8vDhw/18lauXFkKFSqUpdnOExMTZcqUKVK8eHGxsLAQFxcXad++vepktBkFzNoensHBwUYdVzu5VI8ePYzKr51cKiwszKj8ORkwBwUFyZUrV6R58+Zib28vDg4O0q5dO6NmeX+V1CaQTUpKkqlTp4qvr69YWFiIp6enDB8+XLXB0VDALPKi9blv377i6uqqTNa6YMECg9e6q1evSocOHcTZ2VmsrKykcuXKekuwpnXq1Clp0qSJODo6iq2trdSqVUt15myR/7UqG/ox5gFNRnIkYH7y5ImyPmu+fPmkU6dOMm7cOBk6dKjUrFlTNBqNuLi4KPkNBcwiLwJRS0tLsbW11Xn65e3tLba2tsoTtvHjx0ufPn2kbNmyAqh3O8iszhUrVhTgf+swDx06VOrWrau3DnP9+vUFeNFNeuzYsdK1a9cM12E2NmBu2bKl5MuXT1q2bCnDhw+XESNGKMtspb84zJw5UzQajTg7O0vnzp1lzJgx0qNHD+XJb9p1b40JmEX+t4yUoWWmRP43g2NwcLCMHj1a2rRpI+bm5kqXl/QfoMqVK4uJiYn07NlTJk2apLPGckYBs7YriJmZmcFAKS4uTunCXL58eRkwYICMHDlS2rZtK25ublKyZMkMz1dL2ytCbe3wzNZhXrt2rZI3OTlZKlSoIBqNRpo3by6jR4+WJk2aiImJibRs2VLvC8PR0VGKFi0qHTt2lDFjxsiAAQOUBz3Lli1T8qWkpEirVq0EeLFmc58+fWT06NHSsWNHKVKkiFhaWurU+VUEzCIvHsS4urqKRqORBg0ayPDhw2Xw4MHSpEkTsbGx0VsrXESkaNGiUrlyZb30rGLA/G5jwEz06vE6m/WA+U0XGxsrZmZmMmPGjNyuSp6Q0wEzvb1yJGAWefFEde3atdK8eXNxc3NTAsrAwECZPn26TjekjAJmkRdLJVlYWIi9vb0SCKZ/WmlpaSmurq4SFBQkEyZMyNI60FqxsbESGhoqJUuWFAsLC3F2dpbq1avrdU1OSEiQiRMnSokSJZQna23btlVd7iArAfOuXbukS5cu4uvrKzY2NpIvXz4JCAiQRYsW6bUsirxogW/WrJnkz59fzM3NxdPTU+rVqydff/21zk2lsQHzb7/9pryehhaOf/TokQwcOFAKFSokVlZWUr58eVm2bJnyFDR9wPzPP/9IvXr1xM7OTilbK6OA+d9//1Xypw2y03v27Jl8+eWXUrFiRbGxsRFbW1spUaKEhISEyO7duzM8X62UlBTx8vKShg0b6m3TvnaXLl2STz/9VDw9PcXS0lIqVKig2gXk5s2b0qlTJylYsKDY2tpK9erVZdu2bcqT5rQB8zfffCNNmjQRT09PsbCwEHd3d2nUqJHqEISUlBRZvHixVK9eXezt7cXKykqKFi0q7dq1k3Xr1unkfVUBs8iLFvChQ4dKsWLFlM9I+fLlZdSoUXrvf+2YbkPLmWUFb+TebQyYiV49Xmd1W7bLlCmT29V55fbs2SO2traqLc/vko0bNxrswZAdDJjffhoRTkFK754vv/wS48ePx9WrV/WWAKPsGTRoEFavXo1r165lOsNiZrTLShladorebvHx8cqyGOfOneOyUkSvAK+z0Fk2sWDBghg0aFDuVYZemwsXLugs1VqhQgW0atUq2+VpNBoEBQUhIiLi5StHeRIDZnonJSQkoHjx4mjVqhUWLFiQ29V5492+fRtFixbF5MmTMWrUqJcujzdyRESvFq+zRDmDAfPb76XXYSZ6E1lbW2P58uU4fvw4RAQajSa3q/RGu3HjBsaNG4fBgwfndlWIiIiIXhu2Pb792MJMRHkOWz6IiF4tXmeJiIxjktsVICIiSishIQEBAQEICAhAQkJCbleHiIiI3mHskk1ERHlKamoqjh8/rvxORERElFvYwkxERERERESkggEzERERERERkQoGzEREREREREQqGDATERERERERqWDATERERERERKSCs2QTEVGekz9//tyuAhEREREDZiIiyltsbW1x79693K4GEREREbtkExEREREREalhwExERERERESkggEzERHlKQkJCQgODkZwcDASEhJyuzpERET0DuMYZiIiylNSU1Oxb98+5XciIiKi3MIWZiIiIiIiIiIVDJiJiIiIiIiIVDBgJiIiIiIiIlLBgJmIiIiIiIhIBQNmIiIiIiIiIhWcJZuIiPIcGxub3K4CEREREQNmIiLKW2xtbREXF5fb1SAiIiJil2wiIiIiIiIiNQyYiYiIiIiIiFQwYCYiojzl2bNnaNq0KZo2bYpnz57ldnWIiIjoHcYxzERElKekpKRgx44dyu9EREREuYUtzEREREREREQqGDATERERERERqWDATERERERERKSCATMRERERERGRCgbMRERERERERCoYMBMRERERERGp4LJSRESUp9ja2kJEcrsaRERERGxhJiIiIiIiIlLDgJmIiIiIiIhIBQNmIiLKU549e4Z27dqhXbt2ePbsWW5Xh4iIiN5hDJiJiChPSUlJwbp167Bu3TqkpKTkdnWIiIjoHcaAmYiIiIiIiEgFA2YiIiIiIiIiFQyYiYiIiIiIiFQwYCYiIiIiIiJSwYCZiIiIiIiISAUDZiIiIiIiIiIVZrldASIiorRsbGwQGxur/E5ERESUWxgwExFRnqLRaGBra5vb1SAiIiJil2wiIiIiIiIiNQyYiYgoT0lMTESPHj3Qo0cPJCYm5nZ1iIiI6B3GgJmIiPKU58+fY/ny5Vi+fDmeP3+e29UhIiKidxgDZiIiIiJ65/j4+ECj0UCj0cDOzi63q0OvyaZNm5S/u0ajQY8ePXK7SpTHMWAmIiIiIgDA/Pnz0a1bN/j5+cHExAQajQbPnj3LcjlXr15Ft27d4ObmBktLSxQvXhxhYWGqwyyuXLmCzz77DO+//z7y588PjUaDjh07Gn2sEydOwMzMDBqNBt9++22W6unt7Y3Q0FCMHz9eJz06OhpTp05Fq1atUKhQIWg0GgQGBmapbAAICwvTCc7S/qQvLzIy0mBe7U/v3r119pkyZQrq1asHT09PWFlZwd3dHcHBwVi/fj1EJMv1TUtEsGPHDvTr1w9lypSBnZ0d7O3tUb16dfzwww9ZKj8r9bx//z6+/fZbNGnSBN7e3rC0tISbmxs6dOiA06dPq5YfGxuLsWPHwtfXV8nfq1cv3L59Wy+vn58fQkNDMXTo0Ky9IG+J58+fY/r06ShRogSsrKzg5eWFkSNHKqtTGCsmJgb9+/eHu7s7rKysULZsWXzzzTcG3xeRkZHo1KkT8ufPDxsbGwQEBGD16tUGyz99+jSaNWuGfPnywc7ODnXq1MHu3btV8+bUdcsQzpJNRERERACgBBHe3t5wdHTEo0ePslzGhQsXUKNGDTx8+BDNmjVDyZIlcfz4cUycOBGHDx/Gjh07YGLyvzab/fv3Y/LkybCwsICvry/u379v9LGSk5PRq1cvWFlZIS4uLst19fHxQVhYmF76uXPnMGHCBJiamqJ06dK4detWlstOKyQkBD4+Pjppnp6eOv93cnJCaGio6v6//PILLl68iPr16+ukL1q0CJ6enmjcuDEKFCiABw8eYNu2bWjbti2GDBmC+fPnZ7vOiYmJaNq0KWxsbFCvXj00a9YMjx49woYNG9C7d28cPHgQ33//vVFlZaWea9euxcCBA+Ht7Y169eqhYMGCOH/+PNauXYtNmzZhx44dqFevnpI/Pj4eQUFB+Ouvv1ClShW0bNkS//33H1asWIHdu3fj6NGjcHNzU/L7+fkhLCwMkZGRmDdvXrZfnzdV165dsXr1alSuXBlDhw7FhQsXMHv2bBw6dAgRERGwsLDItIxHjx6hVq1auHjxIlq0aAE/Pz/s2bMHH374Ia5cuYJZs2bp5I+KikK1atVw//59tG/fHp6enti8eTM6duyI6OhoDBkyRCf/iRMnUKdOHYgIOnfuDAcHB6xZswaNGjXChg0b0LJlS538OXHdypAQvaM+/vhjcXV1lfj4eCUtNDRUAEh4eHjuVSyLgoKCxNvbWyctJCREXufH+9SpUwJANm7cmCPllS5dWkqXLp0jZdGbJzY2VgAIAImNjc3t6hC9lQxdZ3fs2CH37t0TkRffLwAkISEhS2U3atRIAMiiRYt00kePHi0A5IcfftBJv3r1qhw5ckSePXsm165dEwDSoUMHo441adIksbOzk0mTJgkAWbhwodH19Pb2lqCgINVtd+7ckf3790tcXJyIiACQatWqGV22Vk7cVzx79kycnJzE0dFR555FRFT/NrGxsVKmTBkBIFFRUdk+blJSkkyfPl0eP36skx4TEyNFihQRAHLo0CGjyspKPffu3Ss7duyQ1NRUnfzr168XAFKyZEmd9OnTpwsA6dKli6SkpCjp27dvFwDSvXt31Tpp32shISFGncObJj4+Xs6cOaOTpn1NGjZsKM+fP1fSJ06cKABk3rx5RpWt/SxPmjRJSUtOTpYGDRqIRqORU6dO6eRv166dAJAVK1YoabGxseLv7y82NjZy+/ZtnfxVq1YVExMTiYiIUNLu3r0rbm5u4uHhofd+yonrVkbYJZteiohg/fr1aNmyJdzd3WFhYQEnJyfUqFEDM2bMwIMHDwAAbdu2hUajwZ49ezIsb968edBoNJgyZQoAICIiQqcrkomJCRwcHFCsWDG0adMGS5cuRXx8fJbrffPmTXz11VcYMWIErK2ts37ipKN8+fJo1qwZPvnkE6SkpOR2dYhyXVxcnHLdyk6r1+vyptSTXh9tt+jsSkhIwJ49e+Dm5oa+ffvqbAsNDYWJiQkWL16sk16kSBFUrVoVlpaWWTrWuXPnMGXKFEydOhWFCxfOdp3VuLq6olatWrCxscnRcrNj8+bNePToEdq3b693z2JlZaWX39bWFg0bNgQAXLt2TWfb7du3ceHCBaPunczNzTF27Fg4ODjopLu4uKBfv34AgD///NOoc8hKPd977z28//770Gg0Ovlbt26NkiVL4uLFi4iJiVHSt27dCgCYMGGCTs+FJk2aoGrVqli9ejWePn1qVD2zQ6PRIDg4GFevXkWLFi3g6OgIR0dHtG/fHv/9998rO64hhw8fRv/+/eHm5qbX0rt06VIAwMSJE2Fqaqqkjx49Gg4ODliyZEmm5aempmL58uVwdHTE6NGjlXQzMzOEhYVBRPDDDz8o6Y8ePcLGjRtRsmRJdOvWTUm3tbXF2LFjER8fj1WrVinpZ8+exdGjR9GgQQMEBQUp6QULFsSQIUNw69Yt7Ny5U6dOL3vdygwDZsq2p0+fokmTJmjbti3++OMPBAcHY+TIkQgJCUFqairGjx8PX19fAC+6IgHAihUrMixz+fLl0Gg06N69u056zZo1ERoais8++wz9+vVDhQoVsG/fPvTq1QtlypTBkSNHslT3L7/8EiKCAQMGZGm/N8X06dNx/vz513rM4cOH49y5c1i/fv1rPS7Ru0BEEB4ejj59+qBFixbo06cPwsPDX3qMIlFOe/DgAZ4/fw4vLy+9gMfW1hb58+fH0aNHX3p8YWpqKnr16oUKFSpg8ODBL1XW67Bv3z7MmDEDs2fPxu7du7P0cHn58uUA/ncvlZnExERERETAzMwMJUuW1Nk2btw4lCpVCkePHjW+8irMzc0BvAiSsiujemZ23LTBXnR0NIAX3XHT8/HxQWJiIg4fPpztehrjwYMHqFOnDp48eYJBgwYhKCgIa9euRe3atZXGo1fp9u3b+OKLL1CqVClUr14dixcvRsWKFdGpUyedfPv374ednR2qVq2qk25tbY2aNWvizJkzePLkSYbH+vfffxEdHY2aNWvqPQgJDAyEnZ0d9u/fr6QdPnwYz58/1+lGr6UdYpA2v/Z3Y/O/DhzDTNnWtWtX7Nq1C23atMGSJUvg6Oios/3kyZPo378/gBdPfgoWLIgNGzbgm2++ga2trV55//zzD06ePIm6devCy8tLZ1utWrX0xhglJCRg+vTpmDx5Mpo0aYK//vpL9WKZ3rNnz7BixQo0adJE76np28Ld3R3u7u6v9ZjBwcFwc3PD999/j/bt27/WY9PbxcbGRrkBygutO7nt4cOHaN26NSIiInTSlyxZguDgYGzYsAH58uXLncoRpePk5AQTExPcuHEDIqITNMfFxSEmJgapqam4evUqSpcune3jzJ07FydOnMBff/2l06qYV6W/hylRogTWrl2LcuXKZbjfnTt38Ouvv6J48eKoWbOmwXzTpk1DYmIiYmJisHPnTly7dg2ff/65ztjdnJKamoqVK1cCeNEanBUvU8+//voL//zzDypWrKhzzXNxccHly5cRFRUFPz8/nX0iIyMBAJcuXUKDBg2yVNes+Pvvv9GvXz8sWrRISZs+fTrGjx+PKVOmYPbs2Ur63LlzjR5j6+TkhGHDhqluS0pKwtatW7F06VLs2rULKSkpKFq0KMLCwlTHzMfGxuLu3bvw9/dX/cwUK1YMAHD58mVUqlTJYJ0uX76skz8tExMT+Pj4KHkyy1+wYEHY2dkZnT9tHV+rHOvcTe+UnTt3CgDx9/eXxMREg/nSbhs+fLje+IW0Ro0aJQBk+fLlSlp4eLgAkDFjxhg8Rv/+/QWA9O7d26i6r127VgDIypUr9balHWs0b9488fX1FUtLS/Hz85PFixfr5T9z5owMGTJEypQpI/b29mJjYyNVqlSRZcuWqR57x44dEhwcLAUKFBBLS0spVKiQNG/eXA4cOKCXd8WKFVK9enWxs7MTW1tbqVWrluzatUsvn7FjmNOe23fffSd+fn5iYWEhxYsXlyVLlqjW9/79+zJixAgpWrSoWFhYiKurq/Ts2VNu3rypmr9///6i0Wjk1q1bqtuNxTHM9KbLqXHYqampEhwcrJSl9hMcHKw31u9115PePMZcZ7M7FrB27doCQO97c+zYscr77eDBg6r7GjOG+fLly2JjYyPjxo1T0pYuXZqjY5jTQzbHMG/cuFF+/PFHiYqKkoSEBLlw4YIMGzZMTExMxM3NTR48eJDh/jNnzhQAMnny5Azz2draKq+tmZmZzJw5M8t1NdaUKVOUMcNZld16xsXFSfny5UWj0civv/6qs+3TTz8VANKtWzeda6H2XhWATJs2Ta/MnBrDrD2X9PdG8fHx4uzsLG5ubjrp3t7eGV7T0/6kv78TETl58qR89NFH4uLiIgDE3t5eevbsKfv27cvwu+DmzZsCQGrWrKm6ffz48QJA9u7dm+H5/vTTTwJAPvnkE9XtNWrUEADKePKpU6eqXg+0PDw8xMvLS/l/3759BYD8/vvvenmTkpIEgNSpU8dg/TiGmfIMbfeg4cOHZzibXtptGXXLTklJwc8//ww7Ozu0adMmS3UZN24cAGDDhg1GdU/UttJUq1bNYJ4vvvgCYWFhaNiwIQYOHIgnT56gb9++Ok8IAeCnn37CmjVrULZsWQwYMAAhISF49OgRevTogc8//1wn7+bNm9GkSRNcunQJbdq0wfDhw1G3bl2cOXMGf/zxh07ewYMHo3v37nj48CFCQkLQrVs3XLlyBe+//77OOI/smDNnDj7++GMEBgaif//+ePr0KXr37o0tW7bo5Ltz5w6qVauGOXPmwM/PDx999BFq1aqF5cuXo3r16rh3755e2YGBgRAR7Nu376XqSEQvRERE6LUsq+XhZ47ykpkzZ8La2hr9+vVDixYtMHr0aNStWxezZs1Sut5mt1VYRNC3b194eHjgs88+y8lqvxKtWrVC165d4eXlBSsrK5QsWRJz5szBiBEjcOfOHb3x3OkZGqqWXmxsLFJSUnD9+nVMnz4dn3zyCbp27ZqTpwIAWLNmDT777DOUKVMGX3/9dZb3z049nz9/js6dO+P06dP45JNPlHHPWsOHD4evry9+/PFHVKtWDaNGjULHjh3RvHlz+Pv7A8j++81Y3t7e8PDw0EmztrZGhQoVcOfOHZ17psjISIiIUT/aFnKtqKgoVKxYEfPnz4evry9WrFiBO3fu4IcffkCdOnX0hkFQzmCXbMqWQ4cOAXjRDddY5cuXR4UKFbB3717cvHkThQoVUrbt3r0bt27dQkhIiGp37Yx4e3ujcOHCuHHjBq5evarahSOtw4cPw97eXhlfrWbfvn04ffq0kueTTz5B+fLllQt7wYIFAbwIbKdMmaIzhiclJQXNmzfHlClTMHjwYOV8li9fDgsLC5w8eRIFChRQ8osIHj58qPx/+/bt+Prrr9G9e3csWbJEKXvatGkICAjA4MGD0bJly2x3VT148CBOnjypdNUZPnw4SpYsifnz56NFixZKviFDhuDq1avYsWMHGjdurKRv2bIFLVu2xKeffqq33mXlypWVYxizhmaZMmVU069cuZLp35HeXomJiRgxYgQAYPbs2VmeCCivady4sc54u6y4ePGiUfk6depk9BjAtDhJH70KVatWxcGDBxEaGor9+/fj999/R8WKFfHbb79h9uzZuHjxos73YFYsW7YM4eHh2Lt3r+pEUm+Knj17YubMmRmOrT158iT+/vtv1aFqakxMTFC4cGGMGjUKycnJGD9+PFq1aoW2bdvmSJ23bt2Krl27omjRovj999/1huIZKyv1TE1NRUhICDZv3ozBgwdj8uTJenny5cuHgwcP4rPPPsPWrVsxf/58FClSBF999RXi4uIwatSobL/fjGWofO394pMnT3KkDjY2NvDx8UFkZCTOnDmDXbt2wdXVFfXr18/0oYB2GKKhMcra9Mz+rsaUY29vr9THmPxFihQxqnxj65jTGDBTtty9excA9J6mZSYkJATDhw/HypUrMWbMGCU9qxNapOfh4YEbN24gJiYm00Drv//+y/SiFRISohNQ58+fH0OHDsWYMWOwceNGZWx2+jUUgRcTUfTt2xc7d+7EsWPHdB4qmJubKxNWaGk0Gjg7Oyv/X7hwIczNzfHVV1/pBOL58uXD8OHDMXjwYOzZswfNmzfP8BwM+eijj3TGtRQpUgS1atXCyZMnlbTo6Ghs2LAB7dq10wmWAaBFixaoUqUK1q1bpxcwu7q6AkCuzApJb4/nz5/jm2++AfCit8ebHjAbO4vsy7hz5w7u3Lnzyo9DZKwKFSpg8+bNeul9+vSBk5OTzg1yVpw+fRoAULduXdXtAwcOxMCBAzFnzhyDYz/zAu2MvhnNTq+9N+rRo0eWy9dOmLR///4cCZh//fVXtGvXDh4eHtizZ0+OzZOSUT1FBH369MHPP/+MXr16ZbimdIECBbBw4UIsXLhQJ713794AgIoVK+ZIfQ1R63UH/G9CsrRz5rzMGOYCBQrg6tWrCA8Px9KlS7Fhwwb8/PPP8PDwQNeuXRESEmJwbgA7Ozu4urri2rVrSE1N1Quwr1y5AgAZNiil3a7Nn1ZqaioiIyNRvHhxo/JHR0cjNjZW55gZ5Te2jjmNATO9Vl26dMHHH3+MH3/8UQmYnzx5gk2bNsHb2ztLLdZqjOmK8uDBA9VAN60aNWropVWvXh0AcObMGSUtJSUFixYtwooVK3Du3DnExsbqdAu/ffu28nu7du2wceNG+Pv7o0uXLqhXrx5q1qyp11J89OhRODo66nX/Bl5MWgG8aHXKbsBcvnx5vTQPDw+dLp3Hjx9Hamoq7t+/rzdRCQDEx8fj/v37iImJ0ZnGXzsJx/37942qy9mzZ1XTDbU8E72JatWq9VItzMYEwm5ubtluYX4dAT0R8OK75cqVK+jdu3e2u45Wr14dsbGxeumXLl3CH3/8gZo1a8LPzw9ly5Z92eq+UsePHwegPrMzACQnJytD1Vq3bp3l8rX3Hy8zi7VWREQEPvjgA7i4uGDPnj1GtXYbK6N6fvjhh1i6dCk6deqExYsXZ/k9Ex8fj61bt8LX1xcVKlTIieoaFBUVhVu3buk0JCUkJODUqVNwdXXVaaiZO3cuoqKijCrX29tb78GPRqNB3bp1UbduXXz99ddYvXo1li5dii+++AJffPEFqlSpgpCQEHTq1AkuLi46+9auXRvr1q3D0aNHERgYqFPXAwcOoFy5cplOiFuiRAkULFgQBw4cwLNnz3R6ehw+fBixsbGoXbu2khYYGAgzMzPVpWV3796t1CttHQFgz549OstWGcr/WuTYaGh6p2gnLLh69WqW923RooUAkOPHj4uIyPfffy8A5NNPP9XLa8ykXyIiXl5eAkCuXLmS6fEdHBykTJkyqtu0E2OpTa514cIFASBdu3ZV0rQTE/j5+Um/fv3kk08+kdDQUGXSraVLl+qU8csvv0i1atVEo9EIALGyspKePXvKw4cPlTxmZmaZTgIRFham5M/OpF/ppc+/cuVKoyajiIyM1Cnn8ePHAkAaNWqk+voai5N+vdvehomocuoc9u7da9RnUe1z/TrrSW+enJj0KykpSc6fPy9RUVE66fHx8ZKcnKyTFhMTIxUqVBBra2u5fPmywWMaM+mXmtye9OvRo0dy/vx5uXfvnpKWmJgo586d08v733//ScmSJQWA7N69W7W8zZs3CwDp2bOnwWNev35d53haDx8+lAoVKqjez2i/7429Zhw6dEjs7OykYMGCcv78+Uzz37p1S86fPy9xcXEvVc+RI0cKAPnggw/03ktqHj9+rPP/5ORk6dmzpwCQn376SXWfnJz0C4D069dPJ33atGkCQIYPH/5S5RvrwoULMmbMGPHw8BAAYm5uLtOnT9fJs337dgEgDRs2lOfPnyvpEydOFAAyb948nfz37t2T8+fPy6NHj3TSR48eLQBk0qRJSlpycrI0bNhQNBqNnDx5Uid/u3btBOkm/o2NjRV/f3+xsbGR27dv6+QPCAgQExMTiYiIUNLu3r0rbm5u4uHhkeGEXq9i0i+2MFO2VK9eHVFRUdi3b1+Wu1WFhIRgy5YtWLFiBSpXrqxMApbZhBaGREVF4fr163B2djaqLgUKFMh0TTy1rjXpu9XcuXMH33//PRo3bozt27frdG1ZvXq10pUqrQ4dOqBDhw6IiYlBREQElixZgqVLlyI2NhZr1qwBANjb28PFxUVpTc4N9vb2AIApU6bgk08+MXo/7VjsVz1WiOhdERwcjODg4Awn/goODkZQUNDrqxS91WbMmIELFy4AgPJv3759lV4SM2fOVHoW3bx5E6VKlUJQUJDOe/TEiRNo27YtGjRoAE9PT0RHR2PTpk148uQJfv75Z72hUzExMRg1ahQAKK3HR44cUboi+/n5YezYsa/snA1J3xX6ypUrSlr+/Pkxc+ZMZdvGjRvRs2dPhIaGKj2z4uPjUaZMGVSrVg2lSpVCwYIFcf36dWzduhWxsbEYPHiw6lqzgHFD1U6cOIGOHTuidu3aKFasGJycnHDjxg1s27YNT548QY8ePdCoUSOdfeT/e8EZ0/L84MEDvP/++4iNjUWjRo3wyy+/6OXRXqO0xo0bh+XLlyM8PFxJz2o9ly5dilmzZsHa2hp+fn6YMmWK3nGHDRsGJycn5f+enp6oV68efH19kZSUhJ07d+LSpUsYOnQoOnfunOm5vix/f39s374dwcHBqFGjBv755x9s3boV3t7emDBhwis/PgCULFkSM2bMwNSpU/Hrr79i6dKluHbtmk6eJk2aoH379lizZg2qVauG+vXr4/z589iyZQsCAwMxYMAAnfwLFizAxIkTsXTpUp3Pw/jx47F582Z89tlnOHHiBPz8/LB7926cOHECI0aM0GvR//LLL7Fv3z707NkTu3btQqFChbB582b8+++/mD9/vt6yYgsXLkSdOnXQpEkTdOrUCQ4ODlizZg3u3r2LjRs36s1fkJXrVrbkWOhN7xTtVP3lypWTpKQkg/nUlpxKTEwUFxcXKVCggPz777+i0WgMTnGflWWl0j/ZM6RFixZiYmIiT5480dumbYUdOHCg3rbPP/9c5+n1oUOHBIB89dVXenmHDBmi2sKcXkpKihQvXlxsbGyUpQAaNmwopqamEh0dbdT5vIoW5ps3b4pGo5HmzZsbVQet3377TQDI559/nqX90mML87vtbWj1zMlzePDggcGlpYKDgzNdluZ11ZPeLIaus9rWGUM/165dU/JqW+jSt9JGRUVJmzZtxMPDQ8zNzaVAgQLStm1bvVan9OUY+jGmFfhVtDBnVKf037va44eGhippz549k48++kgCAgIkf/78YmZmJs7OzlK/fn1Zu3atwePev39fLCwspEiRIhkuExQVFSVDhw6VChUqiLOzs5iZmUn+/PmlYcOG8ssvv6juU7lyZSlUqFCG925amf1d0p+viHoLdlbrqb1fMfZ9KPLiXrB48eJibW0tDg4OEhQUJOvWrTPq/HKihTkoKEiuXLkizZs3F3t7e3FwcJB27drJ9evXX6rsl5W2FVkrKSlJpk6dKr6+vmJhYSGenp4yfPhwefr0qV5e7d9C7X723r170rdvX3F1dRVLS0spXbq0LFiwwOB79urVq9KhQwdxdnYWKysrqVy5sqxatcpg3U+dOiVNmjQRR0dHZXlVtaWmRLJ23coOBsyUbdqu1e3bt9frCiPyYo3iqlWrqu47ePBgASDVq1cXAPLdd9+p5ssoYE5ISFDW3nNxcdHrEmaINvBN281DS3thsLGxkUuXLinp9+7dk0KFComVlZXcvXtXRF50qQIgHTt21Cnj0KFDYmlpqXeB2b9/v7ImnVZsbKy4u7uLk5OTkrZx40YBIK1atdLp0qR19OhRnfRXETCLiLRs2VIA9fWqExIS5MiRI3rp2rUZDa2vaSwGzO+2tyGIy+lzSE1NlfDwcOndu7c0b95cevfuLeHh4dlef/lV1ZPeHLzOZq1L9tsgNjZWzMzMZMaMGbldlTwhpwNmenuxSzZl28qVK5VuHb///jsaN24MHx8fxMfH4/jx4zh48KDO7M9p9ejRAwsWLMChQ4dgZWWF9u3bZ3isP//8U+niFBcXh2vXriEiIgL3799HkSJFsHr1aqMnoWjRogXGjBmDvXv3GuzGGBQUhKpVq6JTp06wsLDAmjVrcOvWLcyaNUtZIqBQoUJo1qwZfvnlF9y5cwcBAQG4evUqtmzZgmbNmmHjxo06ZQ4ePBjR0dGoWbMmihQpgmfPnmH79u24ffu2zsRarVq1wkcffYT58+ejRIkSqF+/Ptzc3HDz5k389ddfOHfuHG7fvp3tZaWMtWjRIpw7dw5du3bFN998g8qVK8PU1BSRkZGIiIhAtWrVsGvXLp199u7dC1dXVwQEBLzSuhHldba2tkatC28sjUaj1/UxJ+R0PYneNPv27YNGo4Gtra3qZGJvkyNHjsDS0lJZ6eNdtWnTJnzwwQe5XQ16gzBgpmyzt7fHjh07sH79eqxYsQLh4eG4f/8+rK2tUbp0aUybNs3gRbly5cooW7Ys/vnnH7Rq1SrT9dQOHDiAAwcOQKPRwM7ODgUKFEBQUBCaN2+ODh06wNra2uh6+/n5oWbNmli9ejUmTpyomufjjz/GmTNn8NVXX+H69esoUqQIFi9ejD59+ujkW7lyJcaNG4ctW7bg8OHDKFmyJBYvXgxvb2+9gHns2LFYu3Ytjh8/jm3btsHe3h5+fn6YPn263gODefPmoU6dOli4cCE2b96MhIQEuLm5wd/fH6NHj365cRhGcnV1xbFjxzBr1iysX78eixcvhoWFBTw9PdG5c2e9sV137txBREQERo0alSOzctK7y9raWhl3lZXPNhFRVgwbNkxZ3sfCwiJ3K/Ma1K1b961/KGAMPz8/hIaGKv9/1TNo05tPI3y0TO+gtWvXon379jh48KCyXBS9nC+++AITJkzAxYsXs72+ppZ2WSlDy04REdHL4XWWKGdoNBq9ie/o7cKAmd5JIoKAgAC4ublh27ZtuV2dN96zZ8/g4+ODtm3bYsGCBS9dHm/kiIheLV5niYiMY5J5FqK3j0ajwaJFixAQEICEhITcrs4b7/r16xgwYAA+/fTT3K4KvQWSkpIwevRojB49GklJSbldHSIiInqHsYWZiPIctny82+Li4mBnZwfgxZqstra2uVwjorcPr7NERMZhCzMRERERERGRCgbMRERERERERCoYMBMRERERERGpYMBMREREREREpIIBMxEREREREZEKBsxEREREREREKsxyuwJERERpWVtb459//lF+JyIiIsotDJiJiChPMTExUdaIJSIiIspN7JJNREREREREpIItzERElKckJSVh2rRpAIDx48fDwsIil2tERERE7yqNiEhuV4KIKC1td9yzZ8/mck0oN8TFxcHOzg4AEBsbC1tb21yuEdHbh9dZIiLjsEs2ERERERERkQoGzEREREREREQqGDATERERERERqWDATERERERERKSCATMRERERERGRCgbMRERERERERCq4DjMREeUpVlZWOHr0qPI7ERERUW5hwExERHmKqakpAgICcrsaREREROySTURERERERKSGLcxERJSnJCUlYd68eQCAoUOHwsLCIpdrRERERO8qjYhIbleCiCitMmXKAADOnj2byzWh3BAXFwc7OzsAQGxsLGxtbXO5RkRvH15niYiMwy7ZRERERERERCoYMBMRERERERGpYMBMREREREREpIIBMxEREREREZEKBsxEREREREREKhgwExEREREREangOsxERJSnWFlZITw8XPmdiIiIKLcwYCYiojzF1NQUwcHBuV0NIiIiInbJJiIiIiIiIlLDFmYiIspTkpOT8d133wEA+vXrB3Nz81yuEREREb2rGDATEVGekpSUhMGDBwMAevTowYCZiIiIcg27ZBMRERERERGpYMBMREREREREpIIBMxEREREREZEKBsxEREREREREKhgwExEREREREalgwExERERERESkgstKERFRnmJpaYlt27YpvxMRERHlFgbMRESUp5iZmaFp06a5XQ0iIiIidskmIiIiIiIiUsMWZiIiylOSk5Px008/AQC6dOkCc3PzXK4RERERvasYMBMRUZ6SlJSEnj17AgDatWvHgJmIiIhyDbtkExEREREREalgwExERERERESkggEzEREREb1zfHx8oNFooNFoYGdnl9vVoddk06ZNyt9do9GgR48euV0lyuMYMBMRERERAGD+/Pno1q0b/Pz8YGJiAo1Gg2fPnr1UmU+fPoW3tzc0Gg06duyot/3kyZMYP348AgIC4OLiAisrK5QqVQqffvop4uLiDJZ7/fp19O/fH97e3rC0tISbmxsaNWqE8PBwo+vm7e2N0NBQjB8/Xic9OjoaU6dORatWrVCoUCFoNBoEBgYaf9L/79SpUxg7dizq1asHR0dHaDQajB07NtP9Dhw4gFatWqFgwYKwsrKCj48POnbsiBs3bujkExF89913qFSpEqytreHk5IT3338fR48ezXJdc7L+aUVEROgEqGo/kydPVvKnpKRg5MiRqFWrFtzd3WFpaYnChQvj/fffx549e1SPERsbi7Fjx8LX11d5L/Tq1Qu3b9/Wy+vn54fQ0FAMHTo0ay/GW+L58+eYPn06SpQoASsrK3h5eWHkyJGIjY3NUjkxMTHo378/3N3dYWVlhbJly+Kbb76BiKjmj4yMRKdOnZA/f37Y2NggICAAq1evVs37448/ok+fPihfvjzMzMyg0Whw4cKFLJ9rjhGi1+DatWsCQEJDQ3O7KopvvvlGLC0t5datW0ra0qVLBYAsXbo09yqWRSEhIZL+oxwaGioA5Nq1a6+lDnfu3BErKyuZM2dOjpRXunRpKV26dI6URW+e2NhYASAAJDY2NrerQ/RWMnSd1X72vL29xcnJSQBIQkLCSx1r4MCBYmtrKwCkQ4cOeturVasmJiYmUqtWLRk2bJgMGzZMSpcuLQCkfPny8vTpU719Dh06JA4ODmJnZyedO3eWcePGSb9+/aRSpUoyZcoUo+rl7e0tQUFBqtvCw8MFgJiamoq/v78AkGrVqmXpvEX+931sbW0tJUqUEAAyZsyYDPdZsGCBaDQa8fT0lP79+8vYsWOle/fu4uXlJfv379fJO2DAAAEghQsXlg8//FD69u0rzs7OYm5uLnv37s1yfXOi/uldu3ZNQkNDVX9cXV0FgBw8eFDJn5CQIFZWVhIUFCT9+vWTcePGSY8ePSRfvnwCQGbNmqVTflxcnFSqVEkASJUqVWTEiBHSvn17MTU1lcKFC8vt27cN1guAhISEZPl1eZN16NBBAEjlypXl448/lhYtWggAqV69uiQmJhpVxsOHD6VkyZICQFq0aCEff/yxVK5cWQDIiBEj9PJHRkZKwYIFxdTUVDp16iSjR49W3k/z58/Xy+/t7S0AxN3dXXmPnD9//qXPPbveiYBZ+4FQu0hrjRkzRgBIeHj466tYDgoKCtILmvKSvBYwx8XFiZubmwwcOFAnnQFz9g0ePFgKFCggT548eemyGDC/2xgwE716hq6zO3bskHv37onI/+4tXiZg3rdvn2g0Gpk9e7bBe7EFCxbofV8lJydL69atBYBMnz5dZ9uDBw/E3d1dSpYsKf/9959eecnJyUbVLaOA+c6dO7J//36Ji4sTEcl2wPzPP//IqVOnJDk5WQnCMwo4Dxw4ICYmJtKmTRt59uyZ3va053bo0CEBIH5+fvLo0SMl/caNG+Ls7CzFihUz+rXIqfpnxc2bN8XU1FRKlCiht03t3O/cuSOurq5iY2Oj856cPn26AJAuXbpISkqKkr59+3YBIN27d1c9/tseMMfHx8uZM2d00rSvScOGDeX58+dK+sSJEwWAzJs3z6iyR48eLQBk0qRJSlpycrI0aNBANBqNnDp1Sid/u3btBICsWLFCSYuNjRV/f3+xsbHRe6ixe/du5bOtvc/NzYCZXbLptShUqBDOnz+PwYMH53ZVAADfffcd7ty5g2HDhuV2VV6JwYMH4/z58yhUqNBrO+bw4cNx7949LFy48LUdk95OlpaWWLNmDdasWQNLS8tslREXF6d09cuoS2due1PqSe+O999/H/nz58+RshISEtCnTx+0atUKH3zwgcF8H374IXx8fHTSzMzMMHr0aADAn3/+qbNtwYIFuH37NhYtWqT6PWdm9vKrprq6uqJWrVqwsbF5qXLKlCmjdCs1xqeffgo7Ozv88MMPqte/tOVs3boVADB06FA4Ojoq6Z6enujTpw+uXLmCvXv36ux/+/ZtXLhwAfHx8a+k/lmxcuVKpKSkICQkRG+b2rm7urqiRo0aiI+Px927d5V07eswYcIEmJj8L7Rp0qQJqlatitWrV+Pp06c5Xn8tjUaD4OBgXL16FS1atICjoyMcHR3Rvn17/Pfff6/suIYcPnwY/fv3h5ubG2bNmqWzbenSpQCAiRMnwtTUVEkfPXo0HBwcsGTJkkzLT01NxfLly+Ho6Kh8RoEX782wsDCICH744Qcl/dGjR9i4cSNKliyJbt26Kem2trYYO3Ys4uPjsWrVKp1j1KtX77Xew2aGATO9Fubm5vDz88uxL+GXtXjxYlSqVAklSpTI7aq8Evnz54efn99rXb+2aNGiCAgIMOpiS5QRMzMztGvXDu3atXslN2nZISIIDw9Hnz590KJFC/Tp0wfh4eEGx2oREfDZZ5/h7t27WLBgQbb2136Hpb8OrFu3Dvnz50dQUBCOHTuGWbNmYfbs2fjjjz9eus656cGDBwgPD0eDBg1ga2uL7du3Y8aMGfjmm29Ux29GR0cDeDEOOz3tA4h9+/bppI8bNw6lSpXKsTHOL2P58uUwMTHRCaIy8vDhQxw7dgxOTk46wVRmr0NiYiIOHz6cM5U24MGDB6hTpw6ePHmCQYMGISgoCGvXrkXt2rXx4MGDV3ps4MWDkC+++AKlSpVC9erVsXjxYlSsWBGdOnXSybd//37Y2dmhatWqOunW1taoWbMmzpw5gydPnmR4rH///RfR0dGoWbMmrKysdLYFBgbCzs4O+/fvV9IOHz6M58+fo169enpl1a9fX6lXXsaA2YAbN27A1NQUbdu2Vd2+bds2aDQaTJw4UUnLzhOmHTt2oF69enBycoK1tTWqVKmCn376SS9fjx49oNFocPnyZUyePBnFihWDmZkZli1bBo1Go1wQ006gsGzZMmX/58+fY/78+ahYsSJsbGzg6OhocFIIHx8f+Pj44NGjR+jTp48yOL9BgwaqF+xDhw6hWbNm8PDwgKWlJdzd3dGgQQNs27ZNyRMZGQmNRoOwsDC9/X/++WcEBgbC1tYWDg4OCAoKws6dOw2+BlevXsX06dNRpEgRWFpaoly5cjrHyszx48dx7tw5tG7dOsN8q1atgr+/P6ysrFCkSBF8/vnnSE1N1ckTGRmJsWPHolKlSsiXLx+sra3h7++P2bNn6+UFjHuttIx9b6gJCwuDRqNBZGSkkqZ9ryxbtgybNm1CpUqVlMkepk+frlpOfHw8Jk2ahNKlS8PKygouLi5o164d/v33X9X8rVu3xr///osjR44YVU+iN8HDhw9Rt25d1K1bF0uWLMHWrVuxZMkSJe3hw4e5XUWiPOfYsWOYM2cOZsyYAQ8Pj2yVsWLFCgBA3bp1lbTExEScPXsWRYoUQb9+/VC1alWMGjUKI0eORFBQEN5777039jP5119/QUTg7OyMGjVqoFmzZhg3bhw+/PBDlC5dGsOGDdN5SOfi4gIAiIqK0itL+/1/6dKl11L3rNLei9WtWxeFCxdWzRMfH4+wsDB89tln6NevH0qVKoW7d+/i66+/1nmIkhdeh7///htNmzZFREQEpk+fji1btmDatGmIjIzElClTdPLOnTsXYWFhRv3MnTvX4DGTkpKwfv16NGvWDIULF8aYMWOQlJSEsLAwXL16FREREWjUqJGSPzY2Fnfv3kWRIkV0WuK1ihUrBgC4fPlyhueq3a7Nn5aJiQl8fHx0ysgof8GCBWFnZ5fpMXNdrnUGf42yO4a5UaNGYmFhIffv39fL37p1a9FoNBIZGamkARB/f38pVKiQBAUFydixY6V58+YCQHx8fPTK+fLLLwWAeHl5Sd++fWXIkCHi6+srAGTGjBk6ebX99xs2bChubm7Su3dvGTZsmOzYsUNCQ0OVwfFpJ1I4efKkiIikpKQoA/qrVKkiQ4cOlV69eomTk5NYWlrKvn37dI7l7e0t7u7uUqVKFSlfvrwMGzZM2rVrp0xAkXZM4bFjx8Tc3FycnZ2lV69eMm7cOOnZs6eUKlVK+vfvr/c3SD+GedKkSQJAPD09ZdiwYfLhhx9K/vz5RaPRyJIlS1RfgxYtWoiHh4f069dP+vfvLw4ODmJqaionTpww+PdVe91///13vW3aMcyNGzcWa2tr6dWrl4wePVqKFy8uAGTIkCE6+RcuXCiOjo7SunVrGTFihAwZMkQqVKggAGTAgAE6eY19rdLWMSvvjbTUxjBrz61FixZibW0tnTp1kmHDhkmxYsVUx63ExcVJ1apVBYAEBQXJiBEjpGvXrmJpaSlOTk6qY0m045vSjzfLKo5hfrclJyfLmjVrZM2aNdkef5dT46BTU1MlODhYKUvtJzg4WFJTU3O1nkRZZcx1NrtjmJOSksTf319q1qypfDaMuRdLKyIiQszMzMTX11fi4+OV9Nu3byuTcdnb28vKlSvl0aNH8u+//ypjntu2bWvUMTIaw5wesjmGOa3MxgCvWrVKObeSJUvKH3/8IU+fPpWDBw9KmTJlBIAsWLBAyb9nzx4BIKVKldKZP+TmzZvi4uKi3DfmlJwcwzx48GABID/++KPBPPfu3dO51tra2srKlSv18n366acCQLp166ZzLd65c6ey77Rp0/T2y6kxzADEzMxMbt68qZMeHx8vzs7O4ubmppOuvWc35sfb21vveCdPnpSPPvpI+Rvb29tLz549Zd++fRl+F928eVMASM2aNVW3jx8/XgBkOlncTz/9JADkk08+Ud1eo0YNAaCMJ586daoAkMWLF6vm9/DwEC8vL4PHywtjmN+pgLlMmTIGZ+mrWbOmXsC8Zs0aASBfffWVTnkxMTFiYWEh9erV00nXvrn79eunkz5t2jQBIMOHD1fSTp8+LSYmJlK/fn1lQgmRFx+uGjVqiJmZmdy4cUNJ175ZihYtKnfu3NE7x4wm/VqwYIHyxk77QYqMjBQnJycpVaqUTrr2g9yxY0edCQEmT56sd3EbPny4AJDTp0/rHTcmJkb5XS1gvnDhgpiYmEixYsXkwYMHSvr169fFxcVFbGxsdMrQvgZ+fn466fv37xcA0rt3b9XzT69NmzYCQKcMLW1QqdFo5MCBA0p6fHy8lC9fXm8ig9u3b6veSPTr1080Go1OwGrsa5Xd90ZaGQXMFhYWOg8XHj58KC4uLlKsWDGdMkaNGiUA5LvvvtNJP378uJibm0ujRo30zuPJkycCQJo3b663LSsYML/bciKIzKlAdO/evUbd1GR3wkgGzJRbXmXAHBYWJhYWFnL27FklLSsB8/nz5yV//vxib28vf/31l8427U2/2oPexMRE8fb2Fo1GI1FRUZkeJ68FzNpARKPRKI0eWmfPnhUTExMpWrSoTrr2nsbLy0s+/PBD6devn7i4uCgzezdu3Pil6pyV+hsrMTFRXFxcxN7eXuc+x5Dk5GS5cuWKjBs3TjQajYwfP15n+4MHD5RGhYCAABk5cqR06NBBzMzMlNchfWODSM4GzOnvobTq1q0rACQ6OvqljqEVGRmpvP+rVasmK1asMOo1FGHA/DLyxuCw1+Ts2bM4e/as0flbtmwJFxcXLFu2TGeyqp9//hlJSUno2bOn3j5mZmYIDQ3VSRs2bBhmzpyJVatWYfbs2QBeTDqVmpqKr776SmdCCWtra4wfPx7NmjXDxo0bMWTIEJ2yRo0aBVdXV6PPAQAWLlwIDw8PTJw4ERqNRkn39vZGnz59MHPmTPzzzz/w9/fX2e+LL77QmRCge/fu+PTTT3Hy5El07dpVJ6/apBjaLjKGrFq1CqmpqRg3bhzy5cunpBcuXBgfffQRQkNDsX79evTr109nv/Hjx+uUXatWLRQpUgQnT57M8Hha//33H0xNTeHs7GwwT+PGjVGjRg3l/9q/S4cOHfDLL7+gfPnyAAA3NzfV/QcMGIDvvvsOERER6NGjh862zF6r7L43jNW1a1dUqlRJ+b+TkxNatGiBpUuX4unTp7C3t0dKSgoWL16MatWqoW/fvjr7V65cGS1btsT69evx+PFjnYlG7O3tYW1tbfQkF2XKlFFNv3LlimrXHaLsaNy4sc61LCsuXrxoVL5OnTqhZMmSWS4/JSUly/sQ5WUXL17EtGnTMHbsWJQuXTrL+1+9ehX16tVDfHw8du7ciYoVK+psT/ud07x5c51tFhYWaNiwIRYvXoy//voLXl5e2TuJXKI9t8KFC6NChQo620qXLo1ixYrh0qVLePToEZycnAAAv/zyC2bOnInly5dj8eLFcHZ2RkhICFq1aoU6deqgQIECr/ksMrd9+3bcv38fvXr1MmpSNTMzMxQtWhTTpk3D/fv3MW3aNLRq1QoBAQEAgHz58uHgwYP47LPPsHXrVsyfPx9FihTBV199hbi4OIwaNeqVvw6Gyi9YsCAA4MmTJzlSBxsbG/j4+CAyMhJnzpzBrl274Orqivr166t2s07LwcFBqYsabXraz1h2y7G3t1fqY0z+IkWKZHjM3PZOBczaYEfN2LFj8fnnn+ukWVhYoFu3bpg7dy7+/vtvJaBcunQpHB0dVcfAent7643Vsba2RoUKFbB3717cu3cPBQoUwNGjR2FmZqZan3v37gFQv1FLG+gYIy4uDufOnUOxYsV0FoXXOnfunHKstAFzvnz59MaUaM/r0aNHSlqbNm0wd+5cVKtWDV27dkX9+vVRp06dTD9sAHDmzBkAQO3atfW2BQUFAQBOnz6tt00brKavm9ri9GoePHgAJycnnYcH6aUNlrWqV6+uU2+tn3/+Gf/H3n1HRXH9bQB/lt5BRUBQQUUF1NiVRBSsiUax1yjYNSZGjd1EAXuPsUQTNYhGjb2XxALEXohd0CiisWJXmpS97x++Oz/GncWlCdHnc84e8c6dO3dmZ3fnO3PLL7/8gnPnzuH58+ey/kWZ66TvscrpuaEvXccPeP3eWltb48qVK3j+/DkyMjIU+53fvXsXQgj8888/qFWrlmxZkSJF8Pjx4xzXjyivvTnCbn64f/8+7t+/n+/bISrsoqOjkZqaiokTJ2LixIlay9etW4d169ahdevW2Lp1q2zZzZs30bBhQzx+/Bg7duxAgwYNtNa3tLSEs7Mz7t69q3itoUlLTk7Omx16hzQDkeq6hsq8b5qA2cjICGPGjMGYMWNkeVetWgUAWjccCoOwsDAA0HqgoI/GjRvjl19+weHDh6WAGXgdsC5evFhrpo4+ffoAyP/joLk+e5NmQDJN0Ai87sOc+Vo6K3Z2drIZXYoXL47Y2FiEh4cjNDQUmzdvxpo1a+Ds7Izu3bsjMDBQ540qKysrODo64saNG1Cr1VoB9vXr1wEA7u7uWdZJs1yTPzO1Wo24uDiUL19er/zx8fFISEh46zYL2gcVMOdEnz59MG/ePKxYsQJz5szBhQsXcObMGQwYMADm5uZa+fW9w/T06VOkp6fLBg17k9IUI5py9PXs2TMIIXDt2rVsbSvzB1tDM8BC5ici9erVw759+zBlyhQsWrQI8+fPh5GREVq1aoX58+ejZMmSOrepudOk9MRck6Z0N0pX3fR9UmNubo6UlJQs8yi9j5nfQ42pU6fiu+++g6urK9q2bQsnJyeYmJjg2bNn+PHHH/Hq1Sspr77HKqfnhr70eW81A6acPn0ap0+fzlY9kpOT9T5PdbX40PXkmSgnfHx8cvWEWZ9A2MnJKcdPmN9FQE/0rri5uUlBSmYJCQlYt24dypYti4YNG2oFMHfv3kXjxo1x9+5dbNq0CU2bNtW5DT8/P6xZswbR0dGoV6+ebFl0dDQA/OeeLgOvAwsXFxfExsbi1atXsqmV0tLScP36dZibm+s148i6detgYGDw1gFO37VHjx5h9+7dKFu2LHx8fLK9vuZBhD4zKCQlJWHHjh1wd3fXemKf127evIm7d+/KHpolJyfj7NmzcHR0lF1Xzps3T3GAMiWurq5aU6CqVCpp0MlFixZh3bp1CA0NxcyZMzFz5kzUqlULgYGB6Nq1q1Zrz/r162Pjxo04efIkvL29ZXU9cuQIPvroI8XrxMwqVKgABwcHHDlyBCkpKbKRso8fP46EhATZwzBvb28YGRnhwIEDWmXt379fqlehVmCNwd+hnA76pVGnTh3h6Ogo0tLSpH6ox48f18qHbPRhqF69ujA1NZVNsJ4VTfv9zH1SM9PVh/n58+cCgGjSpIle2xHidZ8epUEGhBBZ9vV4/vy52Llzp+jatavUj0RDqQ9z27ZtBQBx5coVrbIiIiIEADFo0CApLatj4Ovrq7POb2rcuLEAIF69eqW1TNPPd9KkSVrLbt26JQCIFi1aCCFe96mxtrYWVatWlQ1IIoQQx48f19rfzLI6Vjk9NzLLqg9zaGioVhlv5j937pwAIPr27atXHTTUarUwMDAQPj4+2VrvTezD/GFjH2ai/JcXfZhTU1NFdHS0Xv2Fs7oWi4+PF56ensLQ0FCsW7furWVprhGaNm0q+y0/fPiwMDAwECVLlhSpqalvLScv+zA/e/ZMREdHi4cPH+rMo08f4ODgYMXrB814OG8ev+fPn2uVsWzZMgFA9OvXT2uZ5pohJ99Z+tT/7t27Ijo6Wme/2h9//FEAECEhITrLuHr1qnj58qVW+q1bt0TJkiWFgYGBuHz5smzZm8chLS1N9OrVSwAQq1evVtxOXvZhhp5jGOWnmJgYMXr0aOHs7CwACGNjY61BWHft2iWA14PBZR6nKCQkRHFcgIcPH4ro6Gjx7NkzWfrIkSMFADFx4kQpLS0tTTRr1kyxD37Hjh0FALFy5UopLSEhQVSpUkVYWFiIe/fu6dwv9mH+j+jTpw8GDBiA7du3Y/Xq1fD09ETdunUV8+p7h6l27do4c+YMzp49m+1m1ko0zSrebGJhY2ODChUq4Pz581p3gfKajY0NPv/8c3z++eeIj4/HgQMH8ODBA519rqtWrYotW7bg8OHDWvMha+ZjU2o+nFuVKlXCgQMH8M8//+h8knn06FGttGPHjgGA1HT90aNHePnyJZo0aaLV2uDIkSNZ1iGrY5XX50ZOeHp6wsrKCqdOnYIQIsvm65ldu3YNarVaqz880X+Vn58f/Pz8EBERkWUeTTcSov+66dOnS1NIav7t16+f1Epj9uzZ0hPOO3fuwNPTE76+vll+Rt6mQ4cOiI6ORs2aNXH58mWtrkBubm6y5ru+vr748ssvsXjxYtSoUQNNmzbFw4cPsXHjRhgaGuKXX36R5nDOjTebDF+/fl1Ks7e3x+zZs6VlW7ZsQa9evRAUFCSrf0xMDKZPnw4AUmuVXbt2SX/7+Pigb9++Uv6RI0dix44dCAkJweHDh1GtWjVcuHABf/75J0qUKIFZs2bJ6tShQwekpqbio48+grm5OY4dO4ZDhw6hXr160rg5mYn/7zam7xz32a3/2LFjERYWhvDwcPj5+WmVFxYWBpVKhYCAAJ3b3LVrF8aPH48GDRqgTJkysLCwQGxsLHbu3IlXr14hODgYnp6esnVKliyJxo0bw93dHampqdizZw/++ecfDBkyBN26ddNrX3OjSpUq2LVrF/z8/PDJJ5/g4sWL2LFjB1xdXfH999/n+/YBoGLFipg+fTqmTJmCP/74A6Ghobhx44YsT4sWLdCpUyesX78edevWRZMmTRAdHY3t27fD29sbAwcOlOVfuHAhQkJCEBoaKvs8jBs3Dtu2bcOECRMQFRUFDw8P7N+/H1FRUfj222+1nujPmjULkZGR6NWrF/bu3QsXFxds27YNV69exfz587XGBFq2bJnU+krz79ixY6VuCWPGjIGHh0deHDb9FFio/g7l9gnz8+fPhYWFhShRooQAIGbOnKlYBrJxhykqKkoYGBgIb29vxdGaL1++LB48eCD9/21PmDWjJN66dUtr2Q8//CA9rX1ziha1Wq04rZS+T5iPHTum9aQ2LS1NVK9eXRgYGEhTaSk9Yb5y5YowMDAQ5cuXl925un37trC3t9c5SnZunzCvW7dOABArVqzQWpadUbLT09OFmZmZ+OSTT2SjjF+5ckXY29tr7a++xyqn50ZmuX3CLIQQQ4YMEcDrqRjenKYgLS1NHD58WKuc3377TQAQa9as0VqWHXzC/GErTE+YhXg9AquuqaX8/Pxko/wXZD2JskPX96zmqbKuV+bfCc1vuz5PabO6FnvbNDtK5avVarFo0SJRpUoVYWpqKmxtbUWLFi0UWwDq8rYnzFnV6c1rDs1v7JtPhjVPZXW9lJ5uvnjxQowYMUKULl1aGBsbS9OJ3r59WyvvwoULRY0aNYS1tbUwNzcXVapUETNnzlRsRSeEEDVr1hQuLi56PYHPSf2zeoJ94cIF6XszK+fPnxcDBgwQXl5ewtbWVhgZGYkSJUqI1q1biz/++ENxnQEDBojy5csLc3NzYWNjI3x9fcXGjRuz3E5ePmH29fUV169fF61atRLW1tbCxsZGdOzYUfG6/F3K/BRZIzU1VUyZMkW4u7sLExMTUbJkSTFs2DDFp/qa60Ola8eHDx+Kfv36CUdHR2Fqaiq8vLzEwoULdU5tFRsbKzp37iyKFi0qzMzMRM2aNcXatWsV82rOI12vnLbqyikGzP8vq4BZCCECAgIE8HqeNV3NBgD5PMxjx46V5mF2dXXVmod59uzZQqVSiaJFi4pu3bqJ0aNHi549e4patWoJAOLYsWNS3rcFzAsWLJCaC40fP15MmjRJmr4oIyNDtGnTRgAQ5cuXF3379hUjR44UXbp0EWXKlBGmpqaysrITMLdu3VoUKVJEtG7dWgwbNkx8++234qOPPhIARM+ePaV8uuZh1jQBKVWqlBg2bJj4+uuvpXmYly1bJsubVwHzkydPhJmZmQgICNBalt15mDXzCNasWVOMGDFCdOvWTVhZWUnNzTPvr77HSoicnRuZ5UXAnJiYKLy9vQUAUbVqVTFw4EAxfPhw0aFDB+Hk5CQqVqyoVU7v3r2FiYmJYqCfHQyYP2ypqakiNDRUhIaG6n1R96a8DkTVarUIDw8Xffr0Ea1atRJ9+vQR4eHhOZ5/Ob/qSaQvfs9mr0n2+yAhIUEYGRkpTrH0IcrrgJneX2ySrafu3btj5cqVaN68uc6phACgaNGi2Lp1K4YOHYqFCxdCpVKhY8eOmDNnjtY0RsOHD0eNGjUwd+5c/Pnnn3j+/DkcHR1RsWJFLFq0KFvNWgcMGIB//vkHGzZswJQpU6BWq1GyZEl89NFHMDAwwKZNm/Drr7/i119/xbp165CWlgZnZ2fUqlULnTt3zvFx+fLLL2FlZYUTJ05g3759MDU1hbu7O37++Wf07t37retPmDAB5cqVw/z58/Hzzz/DwMAA1atXx5gxY9CiRYsc1ysrRYoUQfv27bF161atQTU0OnfujICAAEydOhX//PMPnJycMG3aNIwaNUqWb/bs2bC1tcWaNWuwYMECuLm5ISQkBG3btsWWLVtkebNzrPLy3MgpCwsLREREYMGCBVizZg1WrlwJlUoFFxcXfPrpp+jRo4csf2pqKjZv3ow2bdq8dUoxoqwYGxvnaPTUzCwtLWUj1ueWSqWSmmfnpbyuJxFlT2RkJFQqFSwtLZGQkFDQ1clXJ06cgKmpKQYMGFDQVSlQW7duRdu2bQu6GvQfohL8pdbLDz/8gG+//RabN2/W+SFTqVS57sdD78bJkydRt25drFmzBl27di3o6rwX1q9fj86dO+PQoUM5GvkyM03f8uzMm05ERPrj96x8eh8TExOMGzeuYCtE70RMTIxs6s5q1aqhTZs2OS6P1//vPwbMekhLS4OXlxdevnyJf//9V+dAEvzA/Le0a9cO165dw7lz5/Qe1Ip0q169OlxcXLBz585cl8ULuQ9beno6/vjjDwDAp59+qvfgNESkP37PEuUNXv+//3gVkoULFy5gx44dOHjwIK5du4Y5c+bkyaiLVDjMmjULq1atwoMHD7JsZk9vFx8fj9atW/NpPeWJV69eoWXLlgBez93KgJmIiAorPnt8//EqJAtRUVH47rvvUKxYMQwZMgRDhgwp6CpRHipXrpzW1BWUMw4ODjyWRERERPTeYcCchZ49e2Zr4BneYSIiIiIiInp/GBR0BYiIiIiIiIgKIwbMRERERERERAoYMBMREREREREpYMBMREREREREpICDfhERUaFiYmKChQsXSn8TERERFRQGzEREVKgYGxvjq6++KuhqEBEREbFJNhEREREREZESPmEmIqJCJSMjA4cOHQIA1K9fH4aGhgVcIyIiIvpQMWAmIqJCJSUlBQ0bNgQAJCQkwNLSsoBrRERERB8qNskmIiIiIiIiUsCAmYiIiIiIiEgBA2YiIiIiIiIiBQyYiYiIiIiIiBQwYCYiIiIiIiJSwICZiIiIiIiISAGnlSIiokLF2NgYM2fOlP4mIiIiKigMmImIqFAxMTHByJEjC7oaRERERGySTURERERERKSET5iJiKhQycjIwN9//w0AqFGjBgwNDQu4RkRERPShYsBMRESFSkpKCurUqQMASEhIgKWlZQHXiIiIiD5UbJJNREREREREpIABMxEREREREZECBsxEREREREREChgwExERERERESlgwExERERERESkgAEzERERERERkQJOK0VERIWKsbExgoKCpL+JiIiICgoDZiIiKlRMTEwQHBxc0NUgIiIiYpNsIiIiIiIiIiV8wkxERIWKWq1GdHQ0AMDT0xMGBry3S0RERAWDATMRERUqycnJqFy5MgAgISEBlpaWBVwjIiIi+lDxtj0RERERERGRAgbMRERERERERAoYMBMREREREREpYMBMREREREREpIABMxEREREREZECBsxERERERERECjitFBERFSrGxsYYMWKE9DcRERFRQWHATEREhYqJiQlmzZpV0NUgIiIiYpNsIiIiIiIiIiV8wkxERIWKWq3GrVu3AAClS5eGgQHv7RIREVHBYMBMRESFSnJyMsqUKQMASEhIgKWlZQHXiIiIiD5UvG1PREREREREpIABMxEREREREZECBsxEREREREREChgwExERERERESlgwExEREREHxyVSiW9KleuXNDVoXdk69atsve+Z8+eBV0lKuQYMBMRERERAGD+/Pno0aMHPDw8YGBgAJVKhZSUlGyVcebMGQQGBqJSpUooUqQILCws4OnpiW+++QZ37tzRyu/m5iYLYN58lStXTmud8+fPo1OnTnBzc4O5uTnKli2L7t2749KlS9mqa9WqVREUFIRBgwbJ0q9fv44JEyagefPmsLe3h0qlQpcuXbJVdlpaGjZu3Iju3bujYsWKsLCwgJ2dHRo1aoStW7fqXG/79u1o2LAhnJycYGVlBS8vL4wZMwaPHj1SzH/u3Dm0bNkSRYoUgZWVFRo0aID9+/dnq666/PXXXxg2bBh8fHxgaWkJlUqFJUuW5LrcYcOGSe+v0vmlVquxcuVKfPzxx7C3t4etrS2qVq2KadOmISEhQZb3yZMnGDx4MOrUqQMHBweYmpqiTJky6NChA6KiorTK9vDwQFBQEIYMGZLr/fgvSk9Px7Rp01ChQgWYmZmhdOnSGD58uNZxfZtHjx5hwIABKFGiBMzMzFC5cmX89NNPEEIo5o+Li0PXrl1hb28PCwsL1K5dG+vWrdNZfnbO67z43sqSIHqL0NBQAUCEh4cXaBl5bdSoUcLR0VEkJSVJaUFBQYWunm/j6+srXF1dZWmBgYHiXX68z549KwCILVu25El5Xl5ewsvLK0/Kov+elJQUMWjQIDFo0CCRkpJS0NUhei/p+p4FIAAIV1dXYWdnJwCI5OTkbJW9ePFiUaJECdGuXTsxZMgQMWLECPHpp58KlUolihYtKq5evSrL/8MPP4igoCCt16effioAiP79+8vyHzhwQBgbGwtzc3PRvXt3MXr0aNGuXTthaGgozMzMxIkTJ/SqJwARGBiouExz3WJiYiK8vLwEANG5c+dsHYfo6GgBQNjZ2YkOHTqIMWPGiMDAQGFpaSkAiEmTJmmtM2nSJAFAlChRQnz55ZdixIgRol69egKAKFOmjHj27Jks/+nTp4WFhYUwNzcXffr0EcOGDRMuLi7CwMBAbN26NVv1VaK5nrCxsRFly5YVAMTixYtzVeaxY8eEgYGBdByUzq8+ffoIAMLd3V0MHjxYDBs2TFStWlUAEHXr1hVpaWlS3ujoaGFlZSWaNWsmvvzySzFmzBjRtWtXYWFhIQwMDMT69esV63Hjxo0sz4H3VefOnQUAUbNmTTFq1Cjh7+8vAIiPP/5YvHr1Sq8ynj59KipWrCgACH9/fzFq1ChRs2ZNAUB8++23Wvnj4uKEg4ODMDQ0FF27dhUjR44UFSpUEADE/PnztfJn97zOi++trDBgzmdqtVps3LhR+Pv7CycnJ2FsbCxsbW3Fxx9/LKZNmyYeP35c0FUUQrwOunQFWO9jwHz79m1hbm4uZsyYIUtnwJxzLVu2FF5eXiI9PT3XZTFgpnchISFB+pFNSEh45+sTFSRd37O7d+8WDx8+FEL879oguxeeum50rVixQgAQvXv31quczz77TAAQR44ckaVr6nXy5ElZ+u+//56twDarYCk2NlacOHFCpKSkSIFVdgPm27dviyVLlmgdv2vXrokiRYoIIyMj8e+//0rpSUlJwtzcXLi4uIinT5/K1hk4cKBisFqnTh1hYGAgIiIipLQHDx4IJycn4ezsnOug4dSpUyI6Olqo1WrpWi43AXNKSorw9PQU/v7+Os8vzfGuXr26LIBTq9WiefPmAoDYs2ePlJ6WliYLoDWio6OFmZmZKFu2rGJd3veAOSkpSZw/f16WtmvXLgFANGvWTHa9FhISIgCIH3/8Ua+yR44cKQCIiRMnSmlpaWmiadOmQqVSibNnz8ryd+zYUQAQK1eulNISEhJElSpVhIWFhbh3754sf3bP67z43soKm2Tno5cvX6JFixbo0KED/vrrL/j5+WH48OEIDAyEWq3GuHHj4O7uXtDVfKu2bdsiOjoaderUKdAy8tKsWbMghMDAgQMLuir5Ytq0aYiOjn6n2xw2bBguX76MTZs2vdPtEuUHIQTCw8PRt29f+Pv7o2/fvggPD9fZ1IzofaFpgpwbpqamiult27YFAMTGxr61jHv37mHfvn2oUKECPvnkE9myuLg4FCtWDLVr15alt2jRAgB0Nl3OjjJlyqBOnTo690UfLi4uGDBgAMzMzGTp5cqVQ6dOnZCeno7jx49L6Q8ePEBycjK8vb1hZ2cnW0dp3y5duoSTJ0+iadOm8PX1ldIdHBwwePBg3L17F3v27JGVc+vWLcTExCAtLU2vfahVqxY8PDygUqn0yv82kyZNwu3bt7Fo0SKdeeLi4gAAjRo1gomJiZSuUqnQvHlzAPLjYGRkBCMjI61yPDw84Onpibi4uHz97lapVPDz80NsbCz8/f1ha2sLW1tbdOrUCbdv38637epy/PhxDBgwAE5OTpgzZ45sWWhoKAAgJCQEhoaGUvrIkSNhY2OD5cuXv7V8tVqNsLAw2NraYuTIkVK6kZERgoODIYTAr7/+KqU/e/YMW7ZsQcWKFdGjRw8p3dLSEmPGjEFSUhLWrl0rpefkvM6L762sMGDOR927d8fevXvRvn17xMXFYe3atZg2bRp+/PFHHD9+HFFRUf+JgNnW1hYeHh6wsLAo0DLySkpKClauXIkWLVrAxsamoKuTL0qUKAEPD493uk0/Pz84OTlh2bJl73S79P4RQuDhw4d4+PBhgQSoT58+RaNGjdCoUSMsX74cO3bswPLly6W0p0+fvvM6Eb0P/vjjDwBApUqV3pr3t99+Q0ZGBgICArSWeXp64vHjxzh16pQsfffu3QBe/x4VdsbGxgAgC/RcXFxgY2OD48eP49mzZ7L8Svt26NAhAEDjxo21ym/SpIksj0ZAQAA8PT0V+5Lnt7Nnz2LGjBmYNm0aSpYsqTNfhQoVYGhoiIMHDyI1NVVKF0Jg7969MDY21rqJoiQuLg5Xr16Fp6dnngX8ujx58gQNGjTAixcvMGjQIPj6+mLDhg2oX78+njx5kq/bBl7fYJo5cyY8PT3x8ccfY+nSpahevTq6du0qy3fo0CFYWVlpPcAyNzdHvXr1cP78ebx48SLLbV29ehXx8fGoV6+e1s0gb29vWFlZyc6748ePIz09Xe/zNCfndX5jwJxP9u7di+3bt6NKlSpYs2YNbG1ttfJUr14dhw8flv4fEREBlUqF4OBgHDhwAPXr14eVlZXsy/Hff/9Fv379UKpUKZiamqJ06dIYNmwYnj9/Lis7KSkJs2fPhq+vL5ycnGBqago3Nzd88803Whd7KpUKkZGR0t+a14oVKwAAK1asgEqlQkREhGJdDx06BB8fH1hYWMDJyQnDhw/XunOZF2UArwcqmD9/PqpXrw4LCwvY2tqiefPmOHnyZJbvR2Y7d+7E06dP0a5duyzzzZ8/H+XLl4eZmRk8PT0VA8ELFy7gm2++QeXKlWFjYwNLS0vUrl0bYWFhimXu2bMHDRs2hIODA8zMzFCyZEn4+/vj6NGjWnlXrVqFTz75BNbW1rCyskL9+vWli4236dmzp9aPQ3BwsPQeLF26FJ6enjA1NUWFChVkdwIze/LkCYYPH45y5crB1NQUTk5O6N27N+7evauV18DAAK1bt8b+/ftx7949vepJpCQpKQkODg5wcHBAUlLSO922EALt2rWTfVdlFhERgXbt2vFJM5Eejh49iuDgYIwZMwatW7dGt27dUL58eYwbN+6t64aFhcHAwEAxYJ4yZQqKFCkCX19f9OjRA2PHjkWHDh3Qo0cPdO/eHSNGjMiP3ckziYmJ2LRpE0xNTVGvXj0p3djYGHPnzsW9e/fg5eWFr776CqNGjUL9+vURFhaG2bNnw8fHR8p/7do1AFAcFE2TpslT0NLT09G7d2/UqlULX375ZZZ5nZ2d8d133+HMmTOoVKkShgwZguHDh6N69eo4evQoVqxYgbJly2qtd/fuXQQHB2P8+PHo2bMnqlWrBpVKhQULFuTXbkkuXLiAzz//HBEREZg2bRq2b9+OqVOnIi4uDpMnT5blnTdvHoKDg/V6zZs3T+c2U1NTsWnTJrRs2RKlSpXC6NGjkZqaiuDgYMTGxiIiIgKffvqplD8hIQEPHjxAmTJlYGCgHQLqe85kdd4ZGBjAzc1NVkZW+R0cHGBlZaV3/oI6r7XbL1Ce0ARMw4YNkzUneZPSskOHDmHq1Klo3rw5vvrqKylPdHQ0fH198ezZM7Ru3Rpubm64ePEi5s2bh8jISBw9elS603Pr1i2MGzcOfn5+aN++PSwsLHDhwgUsXLgQhw4dwvHjx6VmRkFBQVixYgVu3ryJoKAgqR7VqlV7636eOHECM2bMQPPmzfHll1/iwIEDmDt3LoQQmDt3rl7HSt8y1Go12rdvj+3bt6NWrVro378/Xr58ic2bN6NBgwb4888/0aBBg7duT3MhXLduXZ15Zs6ciePHj6Nr164wMTHB+vXr0a9fP7x48QLffvutlG/16tVYv349/Pz80KJFCyQkJGDfvn3o2bMn7t+/j9GjR0t5t23bhjZt2sDFxQXt27eHnZ0d7ty5g7/++gt//fWX7G7p119/jUWLFsHDwwOBgYHIyMjAtm3b0Lx5c6xevVrrjmF2/PDDD/jrr7/Qpk0bNG3aFBs2bECfPn1gb28Pf39/Kd/9+/dRv359XL9+Hc2bN0e7du1w48YNhIWF4cCBAzh9+jSKFy8uK9vb2xs///wzIiMjsz2aKFFhEBERoTNYzpwnMjJSqzkoEckdPXoUISEh0v9r1KiBjRs3wtnZOcv1oqKicOnSJTRu3BilSpXSWl6jRg0cPnwYbdu2xW+//Salf/TRR+jRo4fWU6/CZujQobh37x6+++47rd/RPn36oGjRoggMDMRPP/0kpbdu3RotW7aU5dU8CVRqLadJe/OBytu+3/LLrFmzcPHiRfz999+KwdqbQkJC4OTkhKFDh2L+/PlSeu/evXW2ILh7967sfCtevDhWr16Nhg0b5rr+b2NkZCS7hgZev8+zZ8/G2rVrZdez8+bNw82bN/Uq19XVFUOHDpWlnT17FqGhoVi9ejUeP34Ma2trBAQEoGfPnqhfv77Op+lZnS+Z0988Z3JSzsuXL6FWq2FgYKBX/szbzMl5ne/yrDc0ybi6ugoAIjY2Vu91wsPDpQFklEb0q1WrlrCwsBBnzpyRpf/4448CgJg2bZqUlpCQIOLj47XKWLNmjQAgVqxYIUvP7qBfmeu6c+dOKT0lJUV4eXkJS0tL2aAfeVHGwoULBQDx3XffCbVaLaXHxcUJOzs74enpKUvXpWbNmsLa2lpxmWbQLwsLC/HPP/9I6Q8fPhTOzs7CzMxMPHjwQEr/999/tQaaSE9PF82bNxdWVlaygYDatm0rTExMtN4XtVotG/xt586dAoAICAiQlf3kyRNRrlw5UbRoUZGYmCil6zvol2bf7O3txY0bN6T02NhYYWxsLBo3bizL36FDB2FgYCAbWEMIIbZt2yYAiAEDBog3nT9/XgAQgwcP1lqmRDPozJsvU1NTDvr1AXtXg2ll3o6Pj4/w9fUVTk5OUlpWLycnJ+Hj48NBv+g/S5/BFfNq8JyXL1+KQ4cOCR8fH1G0aFFx7NixLPMPHjxYABCrVq1SXH7o0CFRrFgx0bJlS3HhwgWRmJgozp07Jz799FNhYGAg1qxZo1e9oOeATzkd9EvJrFmzBADRsGFDkZqaqrV8wYIFwtDQUIwdO1bcunVLvHjxQvz555/C3d1dFClSRERHR0t5+/XrJwCIffv2aZWTmpoqAIgGDRrkus4aOR30KyYmRpiZmYnvv/9elp7V+TVq1ChhbGws5s6dK+7duyeePn0qNm7cKBwcHESpUqWkAZ6UvHr1Sly+fFn06dNHGBoa6qxvXg36BUCUK1dOcVmjRo0EAMVr8pyIi4uTfnfq1q0rVq5cKbsmzMqdO3cEAFGvXj3F5ePGjRMAxMGDB7MsZ/Xq1dL1uJJPPvlEABAZGRlCCCGmTJkiAIilS5cq5nd2dhalS5eW/p/b85qDfv2HPHjwAADeehdVSZ06ddCxY0dZ2unTp3H69GkMHjxY68nv119/DQcHB2zYsEFKs7S01LprCQBdunSBra0twsPDs10vJY0aNcLnn38u/d/U1BSdO3dGYmIirl69mqdlLF68GM7OzggJCZHdPXN1dUXfvn0RHR2NixcvvnV7t2/fVjw2mQUGBsr6l9vb22PIkCFISUnBli1bpPSSJUtqDTRhaGiIfv36ISEhQat/lbGxsdRvSUOlUqFo0aKy/TQ2NsaCBQtkZRcpUgTDhg3DkydPcODAgbfupy7ffPMN3NzcpP+XKVMGPj4+OHPmjJQWHx+PzZs3o2PHjvjss89k6/v7+6NWrVrYuHGjVtmOjo4AUCCDXBDlxuHDhxEZGYn79+/rlf/+/fuyLjVEpJuVlRV8fHywa9cuGBoaom/fvjrzpqWlYe3atbC2tlbsOpWamoovvvgCFhYW2LBhAypXrgwLCwt89NFH2LRpE4oXLy4biKgwWbJkCUaOHAlvb29s375d63rg8uXLGDp0KNq2bYupU6eiVKlSsLa2RtOmTbF27Vo8ffpU1rxX87RNqc+pJk2pS+C71r9/f5QuXRrff/+9Xvn/+OMPzJw5E0OGDMGwYcPg5OQEOzs7tG/fHosWLcK///6bZVNlExMTqStdkyZNMHTo0Hzvs63rutLBwQGA8nuUExYWFtI13Pnz57F3714cPnwYarX6retmdb5kTn/bOaNPOdbW1lJLAn3yZ95mYTyv2SS7EKpRo4ZWmqaP7vXr1xEcHKy13MjICFeuXJGlnThxAjNnzsSxY8fw8OFDpKenS8vyqo9p1apVtdI0NwneHLAiN2UkJibi8uXLKFeuHCZNmqSV//LlywCAK1euoEqVKllu78mTJ1kONgFAcTCJjz/+GMDrLyiNjIwM/Pzzz1i5ciUuX76MhIQEWd/GzMe5Y8eO2LJlC6pUqYIvvvgCjRs3Rr169bQGQjt58iRsbW0Vm7T/888/0n62atUqy33QRdfx1vRjB17foFGr1Xj8+LHi+ZaUlITHjx/j0aNHslEJixQpAgB4/PixXnW5dOmSYro+A8IQ5SUfHx8YGhriypUregXNTk5OcHd3Z9BMlA02NjaoVasW9uzZg6dPn0q/GZnt2rULjx49Qu/evRUHCo2JicGtW7fQvn17rabXlpaWqFmzJnbv3q31+1TQQkNDMWjQIFSvXh179uyBlZWVVp79+/cjIyNDNjKwRq1atWBpaYmzZ89KaZob+9evX9fKr0krDIPLnjt3Ds+fP9fZVN7c3BzA6wEX7ezspPFalI6DJi3zcchKo0aN8Mcff+DkyZPSKO354eHDh4rp8fHxAOTNi+fNm6f3NbKdnZ2sSXbx4sURGxuL8PBwhIaGYvPmzVizZg2cnZ3RvXt3BAYGwsvLS7EsKysrODo64saNG1Jz6cz0PWeyOu/UajXi4uJQvnx5vfLHx8cjISFBts3CeF4zYM4njo6OuHnzJu7evYsyZcpka13N3ajMNAN1bdy4UfHJ3psiIyPRtGlTmJub47PPPoObm5v0hTRv3jy8evUqW3XSRal/geapaEZGRp6V8ezZMwghcO3aNVn/lDclJia+dXvm5uZISUnJMo/SnUKlu4Rffvklli5dCg8PD3Tt2hXFixeHkZER4uLiEBYWJjvOXbt2hYGBAX744QfMnDkTM2bMgJmZGbp27Yq5c+dKU0g8ffoU6enpud5PXXQd78x3JzXn2/79+7F///4s65H5giQ5ORnA/378iP4r9u7dC0tLS4SHh6NRo0Zvzb927VrUrl1b8aKXiHTT3EjOPKVNZpoxYAIDAxWXa0ZN1jV1lCY9q/Fj3rW1a9eib9++8PLywp9//qk1ZZRGVvuWmJiIpKQk2X7Vr18fAHDgwAGtp+qa325NnoIUEBCgOIDjrl27cP/+ffTs2ROGhobS2DpZHYfsvr+a801p2qm8pLnmz9yyNDk5GWfPnoWjo6PsujK3fZhVKpU0a8OiRYuwbt06hIaGYubMmZg5cyZq1aqFwMBAdO3aFcWKFZOtW79+fWzcuBEnT56Et7e3rK5HjhzBRx999NYZZCpUqAAHBwccOXIEKSkpshshx48fR0JCguy88/b2hpGRkWLrSKXztDCe12ySnU80TyMzP7XTl1JnfWtrawCvp1kQQuh8acyYMQNGRkaIiorCunXrMGPGDAQHByMoKEg2RP9/hWb/mzRpkuX+6/qBzax48eJvHeJf6U7hm3cJ79+/j2XLluGzzz7DpUuX8PPPP2Py5MkIDg6W5gl8U+fOnXH8+HHEx8djw4YN8PPzQ2hoKPr37y/bV3d39yz3882BJfKa5nhPnjw5y3q4urrK1tME2m9r8k5UWPn5+b11Sho/Pz/FJx9EH5q0tDTpiW9mmbv4ZLZixQqcPXsW3t7eihfljx49wq5du1C2bFmdF8SVK1eWpq15cwCrP/74A6dOnUK1atXe+bSRjx49QkxMjNZgRFu3bkVAQADKlSuH/fv3Z/nUW3PtuHTpUqlrn8aUKVMghJB991SqVAm1a9fGvn37ZNeb8fHxWLBgAZydnbWuR/z8/KBSqaS5jvOa0jzP8+fPx7Jly7ReFStWBPC6K9qyZcukm+2a4/DDDz8gISFBKketVmPq1KkA5E+fL126pHhte/78efz666+wsLCQjS6eH5QedMybNw9PnjxBt27dZOmaeaH1eb3tfbKxsUG/fv1w9OhRxMTEYPTo0bh79y4GDx6MEiVKYPr06bL8vXr1AvB6wN/MD7ZmzZqFFy9eoE+fPrL8Sue1gYEBAgMD8fz5c8yaNUvrGKhUKmk7wOun5G3btsWVK1ewatUqKT0xMRHTp0+HhYWFbDDbnJzX+Y1PmPNJYGAgfv/9d/zwww/44osvtPqpaKSmpup1l0wzGuuJEyfwxRdfvDV/bGwsvLy8tJosnDlzRnoKmJmmWYZSE43CwMbGBhUqVMD58+e17mZlV6VKlbBz5068fPlSCgzfdPToUXTv3l2WduzYMQCQmnxrvvA+//xzrWN25MiRLOtgb2+PDh06oF27dvDw8MCuXbsghIBKpULt2rVx4MABPHz4sMACz1q1akGlUuHEiRPZWk/T5/xtzeKJsmJkZCTd/MrvpwJvUqlU2Lx5s86ppfz8/LB58+Z8n9OTqKBMnz4dMTExACD9269fP+mJ8OzZs6Wg786dO/D09ISvr6/s89KrVy8kJCSgVq1aKF26NBITE3H69GmcPHkSRYoUweLFixW3vXbtWqSlpSEgIEDnZ8zMzAxTpkzBkCFD0LRpU7Rp0wZly5bFP//8g23btsHIyAhz5szJ9XF49OiRND2VJmg7ceIEevbsCQDw8PDAmDFjpPwLFy5ESEgIQkNDpTwxMTHo3Lkz0tPT0aBBAyxZskRrO23atJHGpqlXrx7atm2LLVu2wNPTE23btkWRIkVw7NgxHD16FC4uLhg1apRs/cWLF6NBgwZo0aIFunbtChsbG6xfvx4PHjzAli1btK6XNA9X9P1uPXz4sDStpmYqn7CwMBw/flyqf5s2baT8AQEBiIyMxI0bN2TjpWRHp06dsGDBApw4cQIeHh5o1aoVzMzMcPDgQZw/fx5VqlRBv379pPxLly7Fb7/9Bh8fH7i5uUnda/bu3QshBJYtW6bY/D8vValSBbt27YKfnx8++eQTXLx4ETt27ICrq6vefbdzq2LFipg+fTqmTJmCP/74A6Ghobhx44YsT4sWLdCpUyesX78edevWRZMmTRAdHY3t27fD29sbAwcOlOVXOq8BYNy4cdi2bRsmTJiAqKgoeHh4YP/+/YiKisK3336rNd7SrFmzEBkZiV69emHv3r1wcXHBtm3bcPXqVcyfPx9OTk6y/Nk9r7PzvZUjeTZ8GGnx9/cXAESnTp3E8+fPtZafP39e1KlTR/q/ZtTooKAgrbxqtVpUq1ZNGBsby0aa1nj+/Lls9OwmTZoIGxsb2YjOz58/l0Z19fX1la3fvn17AUDcunVLq+ysRrhWqqtS/rwo44cffhAAxKBBg7RGplar1SIyMlKrHCUzZswQAERERITWsqxGyXZxcZGNkn379m0BQHTp0kVWxrFjx4SpqakAIEJDQ6X0Q4cOSSMGaiQkJIgSJUoIOzs7KW3Lli0CgGjTpo3iyIcnT57M1SjZSuePUv7WrVsLAOK3337Typ+cnCxOnDihlT558mQBQBw9elRrWXboM3orUW5lNRq3Wq0W4eHhok+fPqJVq1aiT58+Ijw8XDYS/7sazZsoP+j6ntWMMKvrlXmWBc0ow29eU4SFhYlWrVqJUqVKCVNTU2FmZiY8PT3FkCFDxO3bt3XWqWbNmkKlUsm2ocvWrVtF48aNhZ2dnTA0NBTFixcXbdu2FSdPntT3EGQ5QrJm33S93txnzW9s5t/9zLOB6Hplzi+EEGlpaWLOnDmiRo0awsLCQhgbGws3NzcxcOBAcffuXcW6nj17VrRo0ULY2toKS0tL4ePjozjCsBBC2Nvby64930ZzPabr9eY1nOb80ec9zGo044SEBDFhwgRp5gwTExNRoUIFMWrUKK1r6kOHDomAgABRvnx5YWVlJUxMTETp0qVF165dFa9VNPJylGxfX19x/fp10apVK2FtbS1sbGxEx44dFa+r36X09HSttNTUVDFlyhTh7u4uTExMRMmSJcWwYcPEy5cvtfIqndcaDx8+FP369ROOjo7S7CYLFy7UOWNNbGys6Ny5syhatKgwMzMTNWvWFGvXrtVZ9+yc19n53soJBsz56MWLF+Kzzz4TAESRIkVE165dxdixY8WQIUNEvXr1hEqlEsWKFZPyZxVACiHE5cuXhaOjo1CpVKJp06Zi2LBh4uuvvxYtWrQQFhYWsml+Nm7cKAAIFxcX8c0334gBAwaI0qVLizp16ghnZ2etL/oFCxZIQ9SPHz9eTJo0SZw7d04IUXgC5oyMDNGmTRsBQJQvX1707dtXjBw5UnTp0kWUKVNGmJqaKh63N0VHRwsAYsKECVrLNF8MzZs3F0WKFBGDBg0SQ4cOFc7OzgKAmDNnjix/y5YtBQDh5+cnRo4cKdq3by+MjY1F27Zttb5gqlatKkqUKCE6dOggRo4cKQYPHizKli0rAIjg4GBZud988430/gUGBorRo0eL7t27Cy8vLwFA3Lt3T8qbXwHz/fv3Rfny5QUA8cknn4jBgweLoUOHijZt2gg7Ozvx6aefapXTqFEj4ejoqHVDI7sYMBMR5S9+z+o/rdT7IiYmRgAQv//+e0FXpVDI64CZ3l9skp2PrK2tsXv3bmzatAkrV65EeHg4Hj9+DHNzc3h5eWHq1KkYMGCA3uV5enri7NmzmD59Onbu3InIyEhYWVmhVKlSGDRoEHr37i3lbd++PVatWoWZM2fil19+QdGiRaVpCj766COtsgcMGIB//vkHGzZswJQpU6BWq1GyZEnFvAXFwMAAmzZtwq+//opff/0V69atQ1paGpydnVGrVi107txZr3I8PDxQr149rFu3TufAWqNGjcL58+exYMEC3Lp1C2XKlMHSpUu1psL47bffMHbsWGzfvh3Hjx9HxYoVsXTpUri6usqmnwKAMWPGYMOGDTh9+jR27twJa2treHh4YNq0aejUqZMs748//ogGDRpg8eLF2LZtG5KTk+Hk5IQqVapg5MiR72TkT0dHR5w6dQpz5szBpk2bsHTpUpiYmKBkyZLo1q2brGkO8LpPd0REBEaMGPHOm9HS+0UIIQ0QY2FhwebPRJRvwsLCEBYWhkqVKuk1NeV/2ZEjR1C6dGl06NChoKtSoLZu3ZqvI2bT+0clRKaRoog+EBs2bECnTp1w9OhRaXAJyp2ZM2fi+++/x5UrV7I9MvybNNNK6Zp2it5viYmJ0ujTCQkJsLS0LOAaEb1/+D0L2bSJDg4OGDRoUMFVht6ZmJgY/P7779L/q1WrJuuHnV0qlUqrHz+9Xxgw0wdJCIHatWvDyckJO3fuLOjq/OelpKTAzc0NHTp0wMKFC3NdHi/kPmwMmInyH79nifIGA+b3H9tN0gdJpVLh559/xs6dO5GcnMx5g3Pp1q1bGDhwIL788suCrgoRERHRO8Nnj+8/PmEmokKHTz4+bHzCTJT/+D1LRKSfwjfhLhEREREREVEhwICZiIiIiIiISAEDZiIiIiIiIiIFHPSLiIgKFUNDQ2meUENDwwKuDREREX3IGDATEVGhYmZmhg0bNhR0NYiIiIjYJJuIiIiIiIhICQNmIiIiIiIiIgUMmImIqFBJTEyESqWCSqVCYmJiQVeHiIiIPmAMmImIiIiIiIgUMGAmIiIiIiIiUsCAmYiIiIiIiEgBA2YiIiIiIiIiBQyYiYiIiIiIiBQwYCYiIiIiIiJSYFTQFSAiIsrM0NAQLVq0kP4mIiIiKigMmImIqFAxMzPDrl27CroaRERERGySTURERERERKSEATMRERERERGRAgbMRERUqCQmJsLS0hKWlpZITEws6OoQERHRB4x9mImIqNBJSkoq6CoQERER8QkzERERERERkRIGzEREREREREQKGDATERERERERKWDATERERERERKSAATMRERERERGRAo6STUREhYqBgQF8fX2lv4mIiIgKCgNmIiIqVMzNzREREVHQ1SAiIiJik2wiIiIiIiIiJQyYiYiIiIiIiBQwYCYiokIlMTERxYsXR/HixZGYmFjQ1SEiIqIPGPswExFRofPo0aOCrgIRERERnzATERERERERKWHATERERERERKSAATMRERERERGRAgbMRERERERERAoYMBMREREREREp4CjZRERUqBgYGKBWrVrS30REREQFhQEzEREVKubm5jh16lRBV4OIiIiITbKJiIiIiIiIlDBgJiIiIiIiIlLAgJmIiAqVpKQkuLm5wc3NDUlJSQVdHSIiIvqAsQ8zEREVKkII3Lx5U/qbiIiIqKDwCTMRERERERGRAgbMRERERERERAoYMBMREREREREpYMBMREREREREpIABMxERERF9cFQqlfSqXLlyQVeH3pF58+bJ3vvg4OCCrhIVcgyYiYioUFGpVPDy8oKXlxdUKlVBV4fogzJ//nz06NEDHh4eMDAwgEqlQkpKSrbKiI+Px5QpU9CmTRu4uLhApVLB29tbZ/4VK1bIApjMLycnJ638Z8+exZgxY9C4cWPY2tpCpVJhzJgx2d5XAKhatSqCgoIwaNAgWfr169cxYcIENG/eHPb29lCpVOjSpUuOtvHo0SMMGDAAJUqUgJmZGSpXroyffvpJ5ywA27dvR8OGDeHk5AQrKyt4eXlhzJgxePTokVbetLQ0TJ8+HV5eXjA1NYW9vT06duyIq1ev5qiub/rrr78wbNgw+Pj4wNLSEiqVCkuWLMlWGY8fP8aSJUvQokULuLq6wtTUFE5OTujcuTPOnTunuE52zsOePXvqPH80r3///VfK7+3tjaCgIAQGBmZrP94XiYmJGDVqFFxdXWFmZgZ3d3dMnjwZaWlp2SonLi4OXbt2hb29PSwsLFC7dm2sW7dOZ/5z586hZcuWKFKkCKysrNCgQQPs379fZ/6IiAg0bNgQ1tbWsLOzQ/PmzXHmzBmtfElJSZg9ezY6deqEMmXK6PzeyA1OK0XZEhcXhzJlyiAwMBArVqwo6OrkqVevXqFChQpo3bo15s+fX9DVKVCaOXAjIiL0yj9//nyMGTMG169fR4kSJfK3cvTes7CwwKVLlwq6GkQfpCFDhgAAXF1dYWtri2fPnmW7jMuXL+P777+HoaEhvLy8cPfuXb3Wa926NapVqyZLs7Ky0sq3detWzJgxA+bm5ihVqhRevHiR7TpqVKtWTfEJ46FDhzBp0iSYmJjA3d0djx8/zlH5z549g4+PD65cuQJ/f394eHjgwIED+Oqrr3D9+nXMmTNHln/y5MkYP348SpQogXbt2sHS0hLHjh3DjBkzsH79epw5cwa2trYAALVajTZt2mD37t3w8PDAl19+iWfPnmH9+vXYt28fjh49Ci8vrxzVW+PXX39FWFgYbGxs4OTkhNjY2GyXsWHDBnz55ZdwdXVF48aN4eDggOjoaGzYsAFbt27F7t270bhxY9k62TkP27RpAzc3N630+/fv4+eff0aFChVQqlQpKd3b2xve3t6IiIhAWFhYtvfnvywtLQ3NmzfHoUOH0LBhQ3Tt2hUnTpzA+PHjcfbsWWzcuFGvcm7evIm6devi8ePH6NSpE0qWLIlt27ahS5cuiI+Px+DBg2X5o6Ki0KBBAwgh0K1bN9jY2GD9+vX49NNPsXnzZrRu3VqWf9euXfD394etra10Y2PNmjWoV68eIiMjUbt2bSlvfHw8Ro4cCZVKhfLly8PMzCyXR0mBoA/SjRs3BADRuXNnnXlGjx4tAIjw8HCt9QIDA3O03cqVKwsAokuXLjrzJCYmilmzZomOHTsKNzc3AUA4OjrmaHvZMWfOHGFiYiL+/ffffN9WYefq6ip8fX1lab6+vkLXV0ZSUpJwcHAQAwYMyJPte3l5CS8vrzwpi4iItOn6nt29e7d4+PChEOJ/3/vJycnZKvv+/fvi0KFDIjExUQghBABRt25dnflDQ0MFABEaGqpX+RcvXhRnz54VaWlpIjw8XAAQo0ePzlYdNfXSdT0TGxsrTpw4IVJSUvS6ZtJl5MiRAoCYOHGilJaWliaaNm0qVCqVOHv2rJSelJQkzM3NhYuLi3j69KmsnIEDBwoAYvHixVLa2rVrBQDh5+cnUlJSpPQzZ84IY2Nj0aBBg2zX902nTp0S0dHRQq1WS+9T5jro4+DBg2L37t1CrVbL0jdt2iQAiIoVK2qtkxfn4fTp0wUAMWXKFMXlmnMnKCgoW+X+Vzx79kzExMTI0hYtWiQAiD59+sjSe/fuLQCIbdu26VV2x44dBQCxcuVKKS0hIUFUqVJFWFhYiHv37sny16lTRxgYGIiIiAgp7cGDB8LJyUk4OzvL3ttXr16JkiVLCgsLC1n9o6OjhYWFhahRo4as7JcvX4r9+/eLZ8+eCSFeX8PmddzAJtn0zkRFReHixYtQqVTYunUrnj9/rphPc6do48aNMDExyZ87RW9ITU3F9OnT0aFDB5QsWTLft1fYHThwACtXrtQ7v7m5OQYMGIDly5fjzp07+VgzotcSExOlpnaJiYl5np/oQ6Vpgpwbjo6O8PHxgYWFRR7VSq5SpUqoWrUqjIzyr6FkmTJlUKdOHZiamua4DLVajbCwMNja2mLkyJFSupGREYKDgyGEwK+//iqlP3jwAMnJyfD29oadnZ2srBYtWgCArFn2jh07AACjR4+W1bNatWpo27Yt/vrrL62m2bdu3UJMTIzezW9r1aoFDw+PXHWPadiwIZo3b65VRrt27VCxYkVcuXJFq7l5XpyHYWFhMDAwQEBAQK7KyUpcXBxUKhV69uyJqKgoNGrUCFZWVrC3t0f//v3x9OnTfNu2ErVajf379+OLL76Ak5MT1q5dK1seGhoKlUqFiRMnytInTpwIlUqF5cuXv3Ubz549w5YtW1CxYkX06NFDSre0tMSYMWOQlJQk2+6lS5dw8uRJNG3aFL6+vlK6g4MDBg8ejLt372LPnj1S+r59+3D79m306NEDFStWlNI9PDzwxRdf4O+//5Y1zbayspK6Z+QXBsz0zmiacA8ZMgQpKSlYv369Yj57e3vs378fT58+xZUrV+Do6Jjvddu6dSsePnyY4/5J75ty5cqhdOnS2VqnS5cuSE9P/+CaN1HeS0pKQqVKlVCpUiUkJSXpvZ4QAuHh4ejbty/8/f3Rt29fhIeH6+wnSESFy99//41Zs2Zh1qxZ2LFjR7b7Thc2V69eRXx8POrVq6d189/b2xtWVlY4dOiQlObi4gIbGxscP35cqwny7t27AQB+fn5SWnx8PIDXzZbfpGmiHBkZKUsPCAiAp6dnobm5bWxsDAAwNDTM03JPnTqF6OhoNGrU6J08CLl+/ToaNmwIKysrDB48GJUrV8bSpUvRrFkzpKam5vv2Y2NjMWHCBJQpUwZNmzbF+vXr0bhxY+lGC/D6tzUqKgqenp5wdnaWre/i4gIPDw/Z+ajL8ePHkZ6ertWMHgCaNGkCALJyNH/nV/53gX2YKceioqIwcuRInDx5EmZmZmjXrh1mzJiBIkWKaOVNTU3F2rVr4eXlhZCQECxZsgRhYWHo16+fVl7NnaLsSEpKwty5c7Fu3Tpcv34dZmZmqFixIrp3746vvvrqreuvXLkSFhYW+PTTT7WWaX50Tp06hWHDhmHPnj149eoVfHx8MHfuXMX+QceOHcO0adNw5MgRJCYmwt3dHf369cM333wju8MaHByMkJAQhIeH4/Lly1i4cCGuXbuGcePGZTlqo5+fH+Li4hAXF6dY3zf7H/v5+SEyMhLJyckYP348Vq9ejSdPnqB69eqYO3cuPv744yzLyFznzH+HhoaiZ8+eAAAvLy9UrFgRq1atwrhx43TWnehthBC4fPmy9Lc+nj59inbt2mn1u1++fDn8/PywatWqvK4mEeWxBQsWyP5fokQJrF69Gg0bNiygGuXOtWvXALy+Cf0mAwMDuLm5SXmA18Hj3Llz0b9/f3h5eaFt27ZSH+aoqCjMnj0bPj4+Uv5ixYoBeN2f1NPTU1a+5vrgn3/+yevdyjN///03Ll68iOrVqyteO+aG5ua95holvx0+fBhTp07F2LFjpbQBAwbgl19+wZIlS/DNN99I6dkZldvNzU3nPiQmJmLjxo0IDQ3FX3/9BSEEqlSpgtmzZ6N79+5aD5xiY2MhhFA8H4HX52l0dDSePHmCokWL6qxTVue1g4MDrKysZOd1Vvk1abnJ/y4wYKYc0dxJ8/Pzw+DBg3Hs2DEsXboUZ86cwZEjR2BiYiLLv3PnTjx+/BhDhw6FjY0NWrZsiY0bN+LatWtwd3fPVV0SEhLg6+uLv//+GzVq1MBXX32FlJQUnD9/HnPmzHlrwKxWq3Ho0CFUr15dq94aqampaNq0KQwMDNCvXz/cunUL69evh4+PD06dOiX7UK9btw5ffPEFihQpAn9/fxQpUgQHDx7E0KFDERMTg8WLF2uVP23aNJw4cQL+/v5o2bIlypYtm6tjokvnzp1x+fJltG/fHi9fvsTatWvRrFkzxMTEwMXFRed6QUFBWLFiBW7evImgoCAp/c3BWby9vREWFob79+/n+QiFRLoIIRSDZY2IiAh069bt3VaKiPRWtmxZLFmyBE2bNoWzszPu3r2LtWvXYvLkyWjVqhXOnj2b62uFgqAZjMzGxkZxuY2NDV6+fAm1Wg0Dg9eNPvv06YOiRYsiMDAQP/30k5S3devWaNmypWz9Zs2aYd26dZg1axYaNWokXcOcP38eW7duBQCt7m/6DuaZ35KSktC7d2+oVCpMnz49T8vWPKSxsbFB27Zt87RsXYoWLYqhQ4fK0iZMmIBff/0Va9eulQXMISEhepfr6+urFTAfPnwYoaGhWL9+PRISEmBvb4+vv/4aPXv2RI0aNXSWpc/5CLw+Z7IKmPUpJ/N5l1X+zNvMaf53gQHzB+7ixYs673QdPnxY53rZuZMG/O9On+aitVu3bti4cSNWrlyp1Y8iu8aNG4e///4bI0aMwKxZs2TL9GlydPnyZbx48QLVq1fXmefevXuoVq0adu7cKf2otWrVCt26dcPIkSOxefNmAK/7H/Xp0weenp6IjIyUvnAyMjLQpUsXLFmyBIGBgVrTa5w8eRKnTp1C+fLls7Xv2fX8+XOcO3dO6lfWpEkT9OjRA6Ghofj+++91rhccHIyIiAjcvHkzyzujNWvWRFhYGI4ePYp27drldfWJFP31119vvQh81823iEh/DRo0QIMGDaT/ly1bFt999x3s7Ozw9ddfY+7cubLg8X22cOFCDB06FKNGjcKXX34JOzs7HD9+HIMGDcLHH3+Mo0ePwsPDAwDQvXt3LFu2DAcPHkS1atXw6aefSqNkV6xYERcuXJCuWQqT9PR0dOvWDefOncP333+PZs2a5Wn5O3fuxJMnT9CnT59860f/purVq8Pc3FyW5uLigtKlS+P8+fOy9Nx0E4qMjJSa5Tdr1gwDBw5Ey5YtpabtlD8YMH/gLl26lKPpW7JzJ+3hw4fYs2cP6tatKz05bdGiBezs7LBy5UqEhITkeDCJtLQ0hIaGwtnZGZMmTdJantVTU43bt28DeN2MJCvBwcGyH56uXbti0qRJ2LFjBxISEmBlZYVVq1YhMTERs2bNkt2dMzQ0RHBwMDZu3IgNGzZoBcz9+/fP92AZACZNmiT78ejatSt69eqlOK9dTmia/2iO6dtUqlRJMf369es6mwwRval3794FXQUiygc9e/bE4MGDcfz48YKuSo5onobpmvbqxYsXsLa2lq4tLl++jKFDh6Jt27aYOnWqlK9p06ZYu3YtateujcmTJ+O3334DAJiYmGDfvn2YOHEiNmzYgEWLFsHZ2Rljx45FhQoV0LlzZxQvXjyf9zJ71Go1AgMDsW3bNnz99deK1265pXlI8y7nWdZ1nB0cHBAbG4uMjIw86addrFgxODo64sGDBzhx4gTc3Nzg6OiITz755K3r6nM+Anjr4Fn6lFOmTBm98ittM7v53wUGzB+4zp074/fff1dcNmbMGMyYMUNxWXbupK1evRppaWmyJpGmpqZo3749li9fLk1MnhNXrlxBQkICmjdvnuPRtJ88eQIAWfafMTY2Rq1atbTSP/74Y0RHRyM6Ohq1a9fGyZMnAbweZfrNH3jNiJRXrlzRKierJjR5qWrVqrL/GxoawtHRMUfzbCrRHMOczldJlBMPHjwo6CoQUT6wsLCAubn5f3Zke00z8uvXr2stU6vViIuLk90s379/PzIyMmQjCWvUqlULlpaWOHv2rCzd0tISM2bM0Lpe0wSiWbWee9eEEOjbty/WrFmD3r17Y/78+Xm+Dc1DmnLlyqF+/fp5Xn5W21USHx8PCwsLWbCcmz7MlStXxu3bt7F7926EhoYiNDQUv/zyC9zd3REQEIAePXoozkkNvG65oVKpFM9H4PV5WqRIkSybYwNZn9fx8fFISEiQdaHIKr8mTVf+N6+PlfK/CwyYKUeycyctLCwMhoaG6Ny5syzvF198geXLlyMsLCzHAbOmD8Obo/1lhybwz2o0zmLFiik2a9I8ldbc8dJMHzB79mydZSn98L/t6XZeUeoPYmRkhIyMjDwpPzk5GQC0bqbooqt1g64nz0RKNHfbiej9Eh0djaSkJMVRoP8LKlSoAAcHBxw5cgQpKSmyG/vHjx9HQkKCLKjTjKb85hRLwOtrh6SkJJ1jrWQmhMCGDRtgY2OT582dc+Orr75CaGgounbtiqVLl+Zqqipd1qxZg7S0tHydSkrJmTNnkJycLLv+uXPnDm7duoWaNWvK8ua2D7ORkRH8/f3h7++Phw8f4rfffkNoaCgmTJiAoKAg+Pr6IiAgAB06dIC1tbW0noWFBWrWrImoqCjcvXtXdu18584dxMTEoFWrVm+tk7e3N4yMjHDgwAGtZfv37wcA2Xmt+fvAgQOy6dWyyj9jxgwcOHAAHTt2fGv+d6HwdWyg/wR976SdP38eZ8+eRUZGBpycnKR5UFUqFRo1agQA2LRpU47vHmuaZNy9ezdH6wP/C/41T5qVPH78GGq1WitdM6WDJhDVfDHdvn0bQgjFV3h4uFY52f3RMDAwQHp6uuIyXU1k3gXNDYPC1gSM/ltUKhVcXV3h6uqq12cj8zymRPTupKWlISYmBrdu3cpVOW8+NQVez/U6YMAAANC64V4YPXr0CDExMbLBiAwMDBAYGIjnz5/LxlhJT0+XuqP16tVLStfMWLF06VKtm4BTpkyBEELr6fObv/lCCAQFBeHChQsYPXo0LC0tZcv9/PygUqkUZ9nIC7rmeR4xYgQWL16Mtm3bYuXKlfnWtzosLAwqleqdB8xPnjzBvHnzZGkTJ06U+mtnpuv6UOn1tvE5ihcvjmHDhuH8+fM4ffo0Bg0ahPPnz6N3795wcnLSmiGiV69eEEJgwoQJsvQJEyZACIE+ffrI0u/du4eYmBjZFI92dnZo27Ytrly5Iis/MTER06dPh4WFBbp27SqlV6pUCbVr18a+fftk05zFx8djwYIFcHZ2RvPmzaX0Jk2aoGTJkli1apWsVWZMTAxWr16N6tWrv/OWE3zCTDmi7500zdzLn332mWJ/4suXL+PYsWPYtGlTjr7cKlasCCsrK8W7t/ry8vKCSqXKcuqFtLQ0nD59GnXq1JGlHz9+HEZGRtJ0DrVr18aWLVtw4sSJfB30ys7ODvHx8Vp9Ym7evCkFrXlN8+OWeTTPN129ehUAUKVKlXypA30YLCwssnUx16BBA/j5+WV5YVG/fn0O/EWkh+nTpyMmJgYApH/79esn/dbMnj0b9vb2AF7/7nt6esLX11fr8/fmU7Hr169Lafb29rKWWA0bNoSrqys++ugjODs74969e9izZw8ePnyI1q1ba5UVExMjjax8//59AMCuXbukv318fNC3b99cHYdHjx5hxIgRAF7PxgEAJ06ckOri4eGBMWPGSPkXLlyIkJAQ2XSLwOuBSbdt24YJEyYgKioKHh4e2L9/P6KiovDtt9/KZpuoV68e2rZtiy1btsDT0xNt27ZFkSJFcOzYMRw9ehQuLi4YNWqUrJ5169aFm5sbPD09YWBggAMHDuDs2bNo3769Vl7gfwNOGRnpFwIcPnwYy5YtA/C/qXzCwsKkbmdt2rRBmzZtpPwBAQGIjIzEjRs3pKbBoaGhmDNnDszNzeHh4YHJkydrbWfo0KGws7OT/p+d81DjwoULOHPmDPz8/HQ2S84vPj4+mDZtGo4dO4bKlSvj6NGjiIyMRM2aNTFw4MB3UoeaNWuiZs2amDNnDrZt24bQ0FCt39J+/fph7dq1WL58OWJjY1GnTh2cPHkS4eHhaNeuHfz9/WX5x44di7CwMISHh8vmAJ81axYiIyPRq1cv7N27Fy4uLti2bRuuXr2K+fPna82UsnjxYjRo0AAtWrRA165dYWNjg/Xr1+PBgwfYsmWL7Prd1NQUixcvRuvWrfHxxx9LNxzWrFkDtVqNJUuWaO37iBEjpJYZjx49QkZGhuxzqIlHckzQB+nGjRsCgOjcubPOPKNHjxYARHh4uNZ6AMTUqVNl+fv37y8AiB9//FEIIURaWppwcHAQFhYW4sWLF4rbuHDhggAgGjVqpLMerq6uwtHRUefywYMHCwBixIgRWsvu3Lmjc73MPD09hZubm87tAxDNmzcXGRkZUvqaNWsEANG2bVvZ9szNzUW5cuXEzZs3tcqKi4sTN27ckP4fFBSkdYz1MXnyZAFArFq1SkpLTU0VHTp0EACEr6+vLL+vr6/Q9XF3dXXVyq+U1r59ewFA3Lp1S2e9mjRpIiwtLUVaWlq29udNXl5ewsvLK1dl0PstISFB+i5KSEgQT548EX5+flJa5pefn5/4999/ZfmJPnS6vmc1vxe6Xpl/wzTXBG/+XgghsizD1dVVlnf8+PGiXr16wtHRURgbGwtbW1tRr1498csvv8h+dzXCw8OzLD8wMFCvY5BV3szXO0qvN/dZ83seGhqqVdbDhw9Fv379hKOjozA1NRVeXl5i4cKFQq1Wa+VNS0sTc+bMETVq1BAWFhbC2NhYuLm5iYEDB4q7d+9q5Z8wYYKoVKmSsLS0FJaWlqJOnTril19+USxbCCHs7e1FnTp13npsNEJDQ7M8DkFBQbL8mvNH6VpH3/Mqczn65hdCiOHDhwsAYsWKFXrtm+Y8enMfskNzngQGBorTp08LPz8/YWlpKYoWLSr69u0rnjx5kuOy80J6erpWWkJCghg5cqQoVaqUMDExEWXLlhUTJ04UqampWnkDAwN1XqfGxsaKzp07i6JFiwozMzNRs2ZNsXbtWp11OXv2rGjRooWwtbUVlpaWwsfHR+zbt09n/oMHDwpfX19haWkprK2txWeffSaioqIU82qu1XW9cosB8wcqtwGzj4+PsLa2Fq1atRJjx46Vvthq1qwpXr16JYQQYvv27QKA6NatW5Z1qV69ulCpVLIAc/jw4SIwMFAEBgYKS0tLYWZmJv3/zR+3Fy9eiOrVqwsAokaNGmLEiBFiyJAholGjRqJMmTJ6HQ/NvsbGxmotc3V1FSVKlBBVq1YVNWrUEGPGjBFdu3YVhoaGokiRIuKff/6R5f/999+FsbGxsLCwEB07dhSjRo0Sffv2FT4+PsLAwED2ZZLTgPnOnTvCxsZGGBsbi4CAAPHNN9+ISpUqiTp16ogSJUrkS8C8YMECAUDUrVtXjB8/XkyaNEmcO3dOWp6UlCSsrKxE+/bts7UvShgw09u8GTALIYRarRbh4eGiT58+olWrVqJPnz4iPDxcqNVqxfxEHzJ+z2YdML+PYmJiBADx+++/F3RVCoW8Dpjp/cU+zJQj5cqVQ3h4OF6+fIn58+fjwoUL6Nu3L/bt2ycNSKEZ1v9tTa0DAwMhhMDKlSultI0bNyIsLAxhYWFITExESkqK9H9NuRrW1tY4dOgQgoKCkJiYiPnz52PVqlVITk5WbI6kRNNnQ9eI4ZqpGzw9PfHzzz9j27ZtaNKkCQ4dOqQ1Ul/nzp1x/PhxtGrVCocOHcIPP/yAnTt3QgiB6dOno0mTJnrVKSvOzs7Yt28f6tSpg3Xr1mHNmjXw8fGRHf+8NmDAAHzzzTe4desWpkyZgvHjx+Pvv/+Wlmum13qz/wtRdiUnJ6N27dqoXbu2NJDcmywtLaU+Xpo+eiqVCn5+fli2bBm2b9+OZcuWSf31lPITEWn6vFauXLmgq5Lvjhw5gtKlS6NDhw4FXZUCNW/ePKhUqhwPOEsfHpUQuZg9m+g98vnnnyM2NhaXL1+WDTSk6QeTXwNkvC9atGiB69ev4/Lly7mea1AzSnZO5gin/77ExERYWVkBeN13kAEuUd7j96x8eh8HBwcMGjSo4CpD78zx48exd+9e6f9+fn6y/rnZERcXhzJlyiAwMDD3/WSp0OKgX0T/b8qUKahRowY2bdr0wd99za4zZ85gz5492LBhQ66DZSIionchO/Ph0vvD29sb3t7eBV0N+g9hwEz0/6pVq4alS5fqnK6JdHv48CFmzJjBGw1ERET0wXBzcwMb677/GDATZcL+tznTrFkzNGvWrKCrQURERESUpxgwE70F+y4TEREREX2YOEo2ERERERERkQI+YSYiokLH3t6+oKtARERExICZiIgKF0tLSzx8+LCgq0FERETEJtlEREREREREShgwExERERERESlgwExERIVKcnIy/Pz84Ofnh+Tk5IKuDhEREX3A2IeZiIgKFbVajcjISOlvIiIiooLCJ8xEREREREREChgwExERERERESlgwExERERERESkgAEzERERERERkQIGzEREREREREQKOEo2EREVOhYWFgVdBSIiIiIGzEREVLhYWloiMTGxoKtBRERExCbZREREREREREoYMBMREREREREpYMBMRESFSkpKCj7//HN8/vnnSElJKejqEBER0QeMfZiJiKhQycjIwO7du6W/iYiIiAoKnzATERERERERKWDATERERERERKSAATMRERERERGRAgbMRERERERERAoYMBMREREREREpYMBMREREREREpIDTShERUaFiaWkJIURBV4OIiIiIT5iJiIiIiIiIlDBgJiIiIiIiIlLAgJmIiAqVlJQUdOzYER07dkRKSkpBV4eIiIg+YAyYiYioUMnIyMDGjRuxceNGZGRkFHR1iIiI6APGgJmIiIiIiIhIAQNmIiIiIiIiIgUMmImIiIiIiIgUMGAmIiIiIiIiUsCAmYiIiIiIiEgBA2YiIiIiIiIiBUYFXQEiIqLMLCwskJCQIP1NREREVFAYMBMRUaGiUqlgaWlZ0NUgIiIiYpNsIiIiIiIiIiUMmImIqFB59eoVevbsiZ49e+LVq1cFXR0iIiL6gDFgJiKiQiU9PR1hYWEICwtDenp6QVeHiIiIPmAMmImIiIiIiIgUMGAmIiIiIiIiUsCAmYiIiIiIiEgBA2YiIiIiIiIiBQyYiYiIiIiIiBQwYCYiIiIiIiJSwICZiIgKFQsLC8THxyM+Ph4WFhYFXR0iek+pVCrpVbly5YKuDr0j8+bNk733wcHBBV0lKuQYMBMRUaGiUqlQvHhxFC9eHCqVqqCrQ/RBmT9/Pnr06AEPDw8YGBhApVIhJSUlR2X9/PPP+Oijj2Bubg4nJyf07dsX8fHxinnT09Mxbdo0VKhQAWZmZihdujSGDx+OhIQErbyZgx2lV+PGjfWuY9WqVREUFIRBgwbJ0q9fv44JEyagefPmsLe3h0qlQpcuXbJ3AP7fo0ePMGDAAJQoUQJmZmaoXLkyfvrpJwghFPPHxcWha9eusLe3h4WFBWrXro1169Zp5cvIyMDw4cPh4+ODEiVKwNTUFKVKlULz5s1x4MCBHNX1TX/99ReGDRsGHx8fWFpaQqVSYcmSJTkq69y5c2jZsiWKFCkCKysrNGjQAPv371fMm5PzcNOmTahbty4sLCxQrFgxdOrUCbGxsVr5vL29ERQUhMDAwBztx39dYmIiRo0aBVdXV5iZmcHd3R2TJ09GWlpatsrR9zzVyO/3P18JIhJCCHHx4kVhYGAgNm3aVNBVKVA3btwQAERQUJDe67Rt21ZUr15dqNXqPKmDl5eX8PLyypOyiIhIm67vWQACgHB1dRV2dnYCgEhOTs52+aNHjxYARIUKFcTIkSNF165dhaGhoXB3dxdPnjzRyt+5c2cBQNSsWVOMGjVK+Pv7CwDi448/Fq9evZLlDQoKUnzVrVtXABBTp07Vq44ARGBgoOKy0NBQAUCYmJgILy8vAUB07tw528fh6dOnomLFigKA8Pf3F6NGjRI1a9YUAMS3336rlT8uLk44ODgIQ0ND0bVrVzFy5EhRoUIFAUDMnz9fljc5OVmYmZkJX19f0b9/fzF27FjRs2dPUaRIEQFAzJkzJ9v1fVNgYKAAIGxsbETZsmUFALF48eJsl3P69GlhYWEhzM3NRZ8+fcSwYcOEi4uLMDAwEFu3btXKn93zcPHixQKAcHFxEcOGDRO9e/cW5ubmwt7eXsTGxiquEx4enu3rnf+61NRUUb9+fQFANGzYUIwePVr4+fkJAKJ9+/Z6l5Od81SI/H//8xsDZspXmuAr88vAwEA4OzuLJk2aiO3bt2e5Tq1atXSWPWLECClfaGiobJmrq6twdHTMVl1btWolKlWqlGdB33+VroAZgPD19VVc5/Tp0wKAWLt2bZ7UgQHzhy0lJUUMGjRIDBo0SKSkpBR0dYjeS7q+Z3fv3i0ePnwohBDC19c3RxeqFy5cEAYGBqJKlSoiMTFRSg8LCxMAxLBhw2T5d+3aJQCIZs2aifT0dCk9JCREABA//vijXtv18PAQBgYG4t9//9Urf1YBc2xsrDhx4oRISUmRfhdzEjCPHDlSABATJ06U0tLS0kTTpk2FSqUSZ8+eleXv2LGjACBWrlwppSUkJIgqVaoICwsLce/ePVl+pe/I+/fvC0dHR2FhYZHrIOPUqVMiOjpaqNVq6SZCTgLmOnXqCAMDAxERESGlPXjwQDg5OQlnZ2etembnPIyPjxeWlpbCyclJPHjwQEoPDw8XKpVKtG3bVnG99z1gfvbsmYiJiZGlLVq0SAAQffr0kaX37t1bABDbtm3Tq+zsnqf5+f6/CwyYKV9pfmQqVaok3QH+7rvvRPfu3YWlpaUAIH744QfFdYyMjAQAcfnyZa1y09PTRYkSJaQ8uQ2Y//77bwFA/PLLLznZzfdKamqqiI6Olr6oNLIKmIUQon79+sLT0zNP6sCA+cP24MED2U22hISEgq4S0XtHn+/ZnF6oam5oK91ErVChgihWrJhIS0uT0jp06CAAiGPHjsnyJiUlCRsbG/HRRx+9dZsnTpwQAETTpk31rmdWAXNmOQ2YMzIyhIODg7C1tdU6hkeOHBEAxDfffCOlPX36VBgZGYmKFStqlbV69WoBQMydO1evbbdt21YAEHFxcbL0mzdviujoaJGampqtfRFC5DhgvnjxogAgPv30U61lU6ZMEQDE5s2bda7/tvNw4cKFAoCYNm2a1rKmTZsKQ0NDrWsaIfImYNacG4GBgeL06dOiYcOGwtLSUhQrVkz069dPsTVFfsrIyBD79u0T3bp1E2ZmZlr7VqtWLaFSqcSdO3dk6bdv3xYqlUr4+/u/dRvZPU/z+/1/F9iHmd6JypUrIzg4GMHBwZg8eTJWrVqFw4cPAwDmzJmjuI6fnx9MTEywcuVKrWX79u3DvXv38Omnn+ZJ/ZYuXQpjY2O0b98+T8r7LzM2NoaHhwfs7e2ztV6XLl0QHR2No0eP5lPN6EMh3ujX99VXXyE8PFxnfz8iKlwOHToEAIp9iRs3bozHjx/j8uXLsvxWVlaoU6eOLK+5uTnq1auH8+fP48WLF1luMywsDAAKVb/Uq1evIj4+HvXq1YOZmZlsmbe3N6ysrKRjBQDHjx9Henq64nFr0qQJAMjy6/L06VOcOnUKdnZ2cHFxkS0LCAiAp6cn7ty5k5NdypGszofs7FdOy8/IyMj3a5Pr16+jYcOGsLKywuDBg1G5cmUsXboUzZo1Q2pqar5uGwBiY2MxYcIElClTBk2bNsX69evRuHFjtGjRQsqTlJSEqKgoeHp6wtnZWba+i4sLPDw89Hofsnue5vf7/y4wYKYCU61aNRQrVgyPHz9WXF6sWDG0aNECv/32G9RqtWxZWFgYHBwc8Nlnn+W6HhkZGVizZg18fX1RtGhR2bK4uDioVCr07NkTUVFRaNSoEaysrGBvb4/+/fvj6dOnimWuWrUKn3zyCaytrWFlZYX69evjjz/+0Mrn5+cHlUqFxMREfPvttyhZsiQMDQ0RERGRZZ01dXqTpr5vjvioUqng5+eH27dvo2PHjrCzs4O1tTXat2+Pe/fuZVlGRESENPBSZGSkbGCVuLg4ab127dpJ+06UU0+fPpXOJY2wsDA0atQIjRo10vmZI6LC49q1a7C2tkbx4sW1lpUrV07KAwAJCQl48OABypQpAwMD7cvSN/MrSU1Nxe+//w4bGxut74+CpKmzZh8yMzAwgJubm2y/ssrv4OAAKysrxeOQlJSE4OBgTJgwAf3794enpycePHiARYsWwcjIKK92J8ey2i993t+CLl8fhw8fxtixY7F9+3ZMmzYNERER6N+/P06fPq01SJrmAZI+rxUrVujcZmJiIsLCwuDn5wd3d3dMmjQJtra2mD17Nm7fvo2dO3fKbkLFxsZCCKF4nIDXx+rp06d48uRJlvua3fO0MLw/uVXwnyL6YJ0/fx6PHz9GvXr1dOYJCAjA1q1bcfDgQeku1IsXL7Bt2zb0798/T34Izpw5g+fPn6Nu3bo682juHPr5+WHw4ME4duwYli5dijNnzuDIkSMwMTGR8n799ddYtGgRPDw8EBgYiIyMDGzbtg3NmzfH6tWr0bVrV63y27Vrh2vXrqF169YAABsbm1zv15uePn2K+vXrw9nZGX369EF0dDQ2b96Mmzdv4tSpUzpHI3Zzc0NQUBBCQkLg6uoqC9Tt7Oykv52cnODq6orw8PA8rzt9GIQQaNeunc4nAREREWjXrh0OHjzI0bOJCrEXL17AwcFBcZnm9+358+dS3szpb8uvZMeOHXjy5An69OkDc3PzHNc7r+mzby9fvoRarYaBgYFe+ZWOQ1JSEkJCQqT/W1paIjQ0FN26ddPK+7Yb8vkhq/3S5/0t6PL1UbRoUQwdOlSWNmHCBPz6669Yu3YtvvnmGyk983v1Nr6+vloPSA4fPozQ0FCsX78eCQkJsLe3x9dff42ePXuiRo0aOsvKzmftzQdI2S0n8/EuDO9PbjFgpnfi4sWL0hPL9PR03Lp1C1u3bkWZMmWwaNEinet9/vnnKFasGMLCwqSAef369UhOTkZAQABOnjyZ67odP34cAFC9enWdeQ4fPoypU6di7NixUtqAAQPwyy+/YMmSJdIX4a5du7Bo0SIEBARg+fLlUkA/depU1K5dG19//TVat26tNbfs8+fPce7cOVhZWeV6f3Q5f/48Ro4ciZkzZ0ppffv2xfLly3HkyBH4+Pgorufm5obg4GCEhIRIf+tSs2ZNbN68GY8fP0axYsXyehfoPRcREfHWi7mIiAhERkbCz8/vndSJiAo/TXNspZZXHwJ7e3sIIaTrq2XLlqFHjx64fPkypkyZUtDV+yBUr15d62aNi4sLSpcujfPnz8vSc9O9KPPvX7NmzTBw4EC0bNkSxsbGOS6T3o5NsumduHTpEkJCQhASEoIpU6Zg1apVSE9PR5cuXXQ2DQEAExMTdO7cGVu2bJHmYly5ciUqVaqU5V207Lh9+zYA6LwbDui+c2hkZIS1a9dKaYsXL4axsTEWLFgge/pdpEgRDBs2DE+ePFGcGzEoKChfg2UAsLKy0gp2AwICALx+yp4XHB0dAUDvvlGVKlVSfF2/fj1P6kP/LatXr9Yr32+//ZbPNSGi3LCxsdHZ51iTbmtrK+XNnP62/G+Kj4/Hnj17UK5cOZ03fguKPvtmbW0tNUXXJ7+u4wAARkZGKFu2LKZOnYp+/fph6tSpOHXqVG52IU9ktV9ve38LQ/n6UOp+ALy+tkxKSkJGRkaebKdYsWLStdaJEyewd+9evd/j3H7WslNO5jIKw/uTW3zCTO9E586d8fvvvwMA1Go17t27h9WrV2Ps2LE4cOAAjhw5orN5dUBAAH766Sds2rQJDRo0wOHDhzF9+vQ8q5umr0aRIkV05tH3zuHJkydha2uLuXPnapXxzz//AACuXLmCVq1ayZblVfCflfLly2s92dYM+vDs2bM82YbmGOrql06Ulfj4+DzNR0QFw93dHSdOnMDDhw+1AgnNDVF3d3cAr2/mOjo64saNG1LT5Kzyv2nNmjVIT08vVIN9aWjqrHQTWK1WIy4uDuXLl9crf3x8PBISEnQehzc1btwYv/zyCw4fPozatWvnpPp5Jqv9etv7q2/5UVFRuH79ulZT4rwoXx8PHz5UTI+Pj4eFhQUMDQ2ltKxa6r3Jzc1N1nKicuXKuH37Nnbv3o3Q0FCEhobil19+gbu7OwICAtCjRw+4ubkpllW2bFmoVCqdDyWuX7+OIkWKZNkcG8j+eZrf7/+7wICZ3jkDAwO4uLhg1KhRuHTpElauXIl169bhiy++UMxft25dVKxYEWFhYdKAVN27d8+z+mgC4ZSUFJ15srpzGBsbi4yMDBgaGuLp06dIT0/Psn9KYmKiYjn5TanviOYmRV7d+UxOTgYAvfuQXbp0STG9UqVKeVIf+m/R93PwLj4vRJRz9evXx4kTJ3DgwAF06dJFtuzAgQMoVqwYvLy8ZPk3btyIkydPwtvbW0pPTk7GkSNH8NFHH+nsLxkWFgaVSiW1mCpMKlSoAAcHBxw5cgQpKSmykbKPHz+OhIQE1K9fX0rz9vaGkZGRYku0/fv3A4Asf1Y0A3oWhkG/NHU+cOAARo4cKVuW3f3SVf66detw4MABrZsD+/fvh6GhIT755JMcl6+PM2fOIDk5WXb9c+fOHdy6dQs1a9aU5c1tH2YjIyP4+/vD398fDx8+xG+//YbQ0FBMmDABQUFB8PX1RUBAADp06ABra2tpPQsLC9SsWRNRUVG4e/eubKTsO3fuICYmRuuBjpLsnqf5/f6/C2ySTQVK88X2tuYkPXr0QEREBJYsWYLGjRtrDYefG5pgOKtRAfW9c2htbQ13d3eI13OcK76CgoK0ysnuAEYqlQrp6ela6W+bdiO/aUYw1nWDgSgrum6avSkvb5gRUc6lpaUhJiYGt27dkqUHBgbCwMAAU6dORVJSkpS+cuVKXL16FQEBAbJArlevXgBed0/KfAN31qxZePHiBfr06aO4/fPnz+Ps2bPw8/ODq6trXu5atj169AgxMTGywYsMDAwQGBiI58+fY9asWVK65sa6SqWS9h14PZBm27ZtceXKFdmME4mJiZg+fTosLCxkA4f+888/Une1zP7991/Mnj0bBgYG0vgvGprZOTLPcpGXbt26hZiYGKSlpUlplSpVQu3atbFv3z5ERkZK6fHx8ViwYAGcnZ3RvHnzHG+zU6dOsLS0xPz582UtkCIjI7F//374+/tne6rM7Hry5AnmzZsnS5s4cSLS09O1Bl/L6hrxzdfbxvUoXrw4hg0bhvPnz+P06dMYNGgQzp8/j969e8PJyUlr5pJevXpBCIEJEybI0idMmAAhhNZn7d69e4iJiZF9jrN7nub3+/8uFPxtJ/qgaQKsN6eNelOPHj0wfvx43L9/X/ajkxc0TzP/+ecfNGvWTDGPvncOa9eujQMHDig2Q8tLdnZ2uHv3rmI984tKpXrr+3T16lVYW1vrbA5ElBU/Pz/4+flleYHg5+cHX1/fd1cpog/M9OnTERMTAwDSv/369ZNuDM+ePVsKPu7cuQNPT0/4+vrKPreVK1fGiBEjMHPmTFSvXh2tW7fGnTt3sG7dOri7u2P8+PGybbZo0QKdOnXC+vXrUbduXTRp0gTR0dHYvn07vL29MXDgQMW65ufcy48ePcKIESMAQApKT5w4IT3t8/DwwJgxY6T8CxcuREhICEJDQ2VPBMeNG4dt27ZhwoQJiIqKgoeHB/bv34+oqCh8++23qFatmmy7s2bNQmRkJHr16oW9e/fCxcUF27Ztw9WrVzF//nw4OTlJeXft2oXx48ejQYMGKFOmDCwsLBAbG4udO3fi1atXCA4Ohqenp6x8zYBT+j55Pnz4MJYtWwbgf1P/hIWFSQOmtmnTBm3atJHyBwQEIDIyEjdu3JBdCyxevBgNGjRAixYt0LVrV9jY2GD9+vV48OABtmzZojVPdXbOw+LFi2PWrFkYNGgQatSogU6dOuHFixdYs2YNihUrhjlz5ui1r7nh4+ODadOm4dixY6hcuTKOHj2KyMhI1KxZU+f5m9dq1qyJmjVrYs6cOdi2bRtCQ0O1boz069cPa9euxfLlyxEbG4s6derg5MmTCA8PR7t27eDv7y/LP3bsWISFhSE8PFw22GZ2zlMgf9//d0IQ5aMbN24IAKJz585ay54/fy7KlSsnAIh169a9dZ0///xTbNmyRSQlJUlpixcvFgBEaGioLK+rq6twdHTUq44PHjwQAETPnj111h+AmDp1qmxZ//79BQDx448/SmlbtmwRAESbNm1EYmKiVnknT56Upfv6+oqcfAybNGkijI2NxfXr16W0+Ph4Ub58eQFABAUFyfIDEL6+vjr3L3N+pTQhhChWrJgoW7aszjqlpaUJc3Nz0axZs2zvz5u8vLyEl5dXrsuh/54nT54IHx8f6XOX+eXn5yeePHlS0FUkei/o+p7V/C7pet24cUPKq/m9UPp9UavVYsmSJaJy5crC1NRUODg4iN69e4sHDx4o1ic1NVVMmTJFuLu7CxMTE1GyZEkxbNgw8fLlS8X8aWlpwtHRUVhZWYmEhIQcHQMAIjAwUHFZ5t9/pdeb+xwUFKR4PSKEEA8fPhT9+vUTjo6OwtTUVHh5eYmFCxcKtVqtuO3Y2FjRuXNnUbRoUWFmZiZq1qwp1q5dq5Xv/PnzYsCAAcLLy0vY2toKIyMjUaJECdG6dWvxxx9/KJZtb28v6tSpk+VxySw0NDTL4/DmtYLm/Ml8nmicPXtWtGjRQtja2gpLS0vh4+Mj9u3bp7jd7JyHGhs2bBC1a9cW5ubmokiRIqJDhw7i2rVrOvctPDxccR+yQ3OeBAYGitOnTws/Pz9haWkpihYtKvr27Vvgv1np6elaaQkJCWLkyJGiVKlSwsTERJQtW1ZMnDhRpKamauUNDAwUAER4eLjWMn3PU438fv/zEwNmyleaL5JKlSqJoKAgERQUJCZMmCD69u0rHB0dpYvgzB/orILsN+VFwCyEEHXr1hWurq466+/j4yOsra1Fq1atxNixY6UPcs2aNcWrV69k63zzzTcCgHBxcRGBgYFi9OjRonv37sLLy0sAEPfu3ZPy5jRg3rFjhwAgHBwcxFdffSUdzzZt2uRbwNy+fXsBQHTo0EGEhISISZMmiWfPnknL//rrLwFALFiwINv78yYGzB+2+/fvy34YAwMDRXh4uM6LSyLKPn7PZh0wv49iYmIEAPH7778XdFUKhbwOmOn9xSbZ9E5cunRJNsCThYUFKlasiG+//RZDhw6VjR5YEPr27Yt+/frh2LFj+Pjjj7WWlytXDvPmzcOIESMwf/58mJqaom/fvpg5cyZMTExkeX/88Uc0aNAAixcvxrZt25CcnAwnJydUqVIFI0eOzJMmJC1btsTKlSsxZcoU/PLLLyhZsiSGDx+ODh06YOvWrbkuX8mPP/6I9PR07N+/H5s2bYIQAt27d5emAli7di3MzMz07odKpEvmKdYSEhJgaWlZgLUhovdZWFgYwsLCUKlSJVy8eLGgq5Ovjhw5gtKlS6NDhw4FXZUCNW/ePAwbNqygq0H/ISohcjF7NtF7IjExEWXKlEHbtm3x888/S+lxcXEoU6YMAgMDsWLFioKrYCGXlJQEV1dXtG7dWurrlBuafuW6RtGm91tiYqIUNDNgJsof/J6VT+/j4OCAQYMGFVxl6J05fvw49u7dK/1fM35GTvA68cPAJ8xEACwtLfH9999j1KhRmDBhAlxcXAq6Sv8pP//8MxISEhRHACciIiqMsjMfLr0/vL29ZdOXEb0NA2ai/zdw4EA8f/4c//77LwPmbLK0tMSKFStQqlSpgq4KERER0Tvh5uYGNtZ9/zFgJvp/JiYmWlNdkH769+9f0FUgIiIiIspzDJiJssA7h0REREREHy4GzEREVKiYm5tLo9Wam5sXcG2IiIjoQ8aAmYiIChUDAwNpBF8iIiKigmRQ0BUgIiIiIiIiKoz4hJmIiAqV1NRUTJ06FQAwbtw4mJiYFHCNiIiI6EOlEhzRiIgKGU1z3EuXLhVwTaggJCYmwsrKCgCQkJAAS0vLAq4R0fuH37NERPphk2wiIiIiIiIiBQyYiYiIiIiIiBQwYCYiIiIiIiJSwICZiIiIiIiISAEDZiIiIiIiIiIFDJiJiIiIiIiIFHAeZiIiKlTMzMxw8uRJ6W8iIiKigsKAmYiIChVDQ0PUrl27oKtBRERExCbZREREREREREr4hJmIiAqV1NRU/PjjjwCAIUOGwMTEpIBrRERERB8qlRBCFHQliIgyq1SpEgDg0qVLBVwTKgiJiYmwsrICACQkJMDS0rKAa0T0/uH3LBGRftgkm4iIiIiIiEgBA2YiIiIiIiIiBQyYiYiIiIiIiBQwYCYiIiIiIiJSwICZiIiIiIiISAEDZiIiIiIiIiIFnIeZiIgKFTMzM4SHh0t/ExERERUUBsxERFSoGBoaws/Pr6CrQURERMQm2URERERERERK+ISZiIgKlbS0NPzyyy8AgP79+8PY2LiAa0REREQfKgbMRERUqKSmpuLrr78GAPTs2ZMBMxERERUYNskmIiIiIiIiUsCAmYiIiIiIiEgBA2YiIiIiIiIiBQyYiYiIiIiIiBQwYCYiIiIiIiJSwICZiIiIiIiISAGnlSIiokLF1NQUO3fulP4mIiIiKigMmImIqFAxMjLC559/XtDVICIiImKTbCIiIiIiIiIlfMJMRESFSlpaGlavXg0A+OKLL2BsbFzANSIiIqIPFQNmIiIqVFJTU9GrVy8AQMeOHRkwExERUYFhk2wiIiIiIiIiBQyYiYiIiIiIiBQwYCYiIiIiIiJSwICZiIiIiIiISAEDZiIiIiIiIiIFDJiJiIiIiIiIFHBaKSIiKlRMTU2xfvGxPLUAAJW2SURBVP166W8iIiKigsKAmYiIChUjIyN07NixoKtBRERExCbZRERERPThUalU0qty5coFXR16R7Zu3Sp773v27FnQVaJCjgEzEREVKunp6diwYQM2bNiA9PT0gq4O0Qdl/vz56NGjBzw8PGBgYACVSoWUlJRslXHmzBmMGzcOtWvXRrFixWBmZgZPT0+MHz8eiYmJWvkPHDiALl26oEKFCrCxsYG1tTWqVKmC8ePH4+nTpzq3c+nSJXzxxRdwdnaGqakpSpYsiTZt2uDcuXN617Vq1aoICgrCoEGDZOnXr1/HhAkT0Lx5c9jb20OlUqFLly76H4RMHj16hAEDBqBEiRIwMzND5cqV8dNPP0EIoZV38uTJaNy4MUqWLAkzMzOUKFECfn5+2LRpk2J+tVqNlStX4uOPP4a9vT1sbW1RtWpVTJs2DQkJCTmqb2Z//fUXhg0bBh8fH1haWkKlUmHJkiW5LnfYsGFSwPrm+bVixQpZQKv0WrVqlWyd7Jy3Hh4eCAoKwpAhQ3K9HwVl06ZNqFu3LiwsLFCsWDF06tQJsbGx2SojPT0d06ZNQ4UKFWBmZobSpUtj+PDhiufN2bNnMWbMGDRu3Bi2trZQqVQYM2aMYrlxcXFvff/69OmTo/0uKGySTURZCg4ORkhICG7cuAE3N7eCrg59AF69eoVOnToBABISEmBkxJ8qondFE0S4urrC1tYWz549y3YZX375JU6dOoVPPvkEAQEBAIA///wTkydPxo4dO3D48GFYWVlJ+ffv348TJ07A29sbLi4uUKvVOHXqFCZPnozffvsNp0+fRrFixWTb2Lp1Kzp37gwbGxu0atUKTk5OePDgAY4cOYILFy6gatWqetW1WrVqCA4O1ko/dOgQJk2aBBMTE7i7u+Px48fZPg4A8OzZM/j4+ODKlSvw9/eHh4cHDhw4gK+++grXr1/HnDlzZPl//vlnlCxZEp999hmKFy+OJ0+eYOfOnejQoQMGDx6M+fPny/L3798fy5cvh7u7O7p16wYjIyMcPHgQ48aNw7Zt23D48OFcfYf++uuvCAsLg42NDZycnLIdlCk5fvw45s+fD0tLS8UbKNWqVUNQUJDiujNmzEBqaioaNmwoS8/Oeevh4YHg4GDExcXhxx9/zPmOFJAlS5bgyy+/hIuLCwYOHIjnz59j7dq1CA8Px8mTJ1GmTBm9yunevTvWrVuHmjVrYsiQIYiJicHcuXNx7NgxREREwMTERMq7detWzJgxA+bm5ihVqhRevHihs1w7Ozud79/vv/+OK1euoEmTJtnb6YImiOg/58aNGwKAACBq1aqlM9+IESOkfKGhoTnaVlBQkAAgbty4kbPK5oCXl5fw8vJ6Z9ujwiUhIUE6bxMSErT+XxgUxjoRZYeu79ndu3eLhw8fCiGE8PX1FQBEcnJytspeuHCh1m9GWlqaaNeunQAgpk2bJlumq/zg4GABQEycOFGWfu3aNWFhYSHq1asnnj17prVeWlqaXvUEIAIDAxWXxcbGihMnToiUlBTpN7dz5856lZvZyJEjtfYhLS1NNG3aVKhUKnH27FlZfqVjkZCQICpVqiQAiJs3b0rpmnpVr15dvHr1SkpXq9WiefPmAoDYs2dPtuuc2alTp0R0dLRQq9UiNDRUABCLFy/OcXkpKSnC09NT+Pv7Z/v8On78uAAgmjZtqrUsJ+et5vjpOgcKo/j4eGFpaSmcnJzEgwcPpPTw8HChUqlE27Zt9Spn165dAoBo1qyZSE9Pl9JDQkIEAPHjjz/K8l+8eFGcPXtWpKWlifDwcAFAjB49Olt1T0lJEXZ2dsLW1lYkJSVla92CxibZRP9hRkZGOH36NKKjo7WWZWRkYPXq1Xw6R/T/hBAIDw9H37594e/vj759+yI8PFyxmSPRh0rTBDk3vvrqK60WSUZGRhg5ciQA4PDhw7JlZmZmiuW0bdsWALSeak6dOhUpKSlYuXIlbG1ttdbLi9+9MmXKoE6dOrkaqV+tViMsLAy2trbSvmvqFxwcDCEEfv31V9k6SsfC0tISzZo1AwDcuHFDSo+LiwMANGrUSPY0UKVSoXnz5gBeNwfP7NatW4iJiUFaWppe+1CrVi14eHhApVLplf9tJk2ahNu3b2PRokXZXjcsLAwAFPsc58V5m1MqlQp+fn6IjY2Fv78/bG1tYWtri06dOuH27dt5uq3169cjMTERQ4YMgYODg5Tu5+eHJk2aYPv27VrvuZLQ0FAAQEhICAwNDaX0kSNHwsbGBsuXL5flr1SpEqpWrZqrz9a2bdvw7NkzdOrUCebm5jkupyAwYCb6D/Pz84OJiQlWrlyptWzfvn24d+8ePv300wKoGVHh8vTpUzRq1AiNGjXC8uXLsWPHDixfvlxKy6qfJBHlDWNjYwD6B7R//PEHgNcX6xpCCGzevBnVq1dH2bJlER4ejpkzZ+LHH39EVFRU3lc6F65evYr4+HjUq1dPKxD29vaGlZUVDh069NZyXr16hYiICBgZGaFixYpSeoUKFWBoaIiDBw8iNTVVShdCYO/evTA2NsYnn3wiKysgIACenp64c+dOLvcu+86ePYsZM2Zg2rRpKFmyZLbWffXqFX7//XfY2NhIN1IKkydPnqBBgwZ48eIFBg0aBF9fX2zYsAH169fHkydP8mw7mvOlcePGWsuaNGmCjIwMHD16VK9yrKysUKdOHVm6ubk56tWrh/Pnz2fZ7DonNDc8AgMD87Tcd4GPnoj+w4oVK4YWLVrgt99+w5QpU2Bg8L97YGFhYXBwcMBnn32GXbt2ydZbs2YN1q9fjzNnzuD+/fuwtbVFgwYNMHHiRHh5eem17fT0dPz0008IDQ3FlStXpB/mkJAQrS9gooIkhEC7du0QERGhuDwiIgLt2rXDwYMH8+wpChFp09zcbdSokeLy3bt34+TJk0hMTMTZs2dx4MAB1KlTB19++aWU5/r163j27BlKliyJli1bav2+derUCStXriwUc7hfu3YNAFCuXDmtZQYGBnBzc5PyvGnq1Kl49eoVHj16hD179uDGjRuYMWMGnJycpDzOzs747rvvMHHiRFSqVAktWrSAkZERDhw4gJs3b2LFihUoW7Zs/uxcNqWnp6N3796oVauW7P3U144dO/D06VP07du3UD6dvHDhAvr374+ff/5ZSps2bRrGjRuHyZMnY+7cuVL6vHnz9B4bwM7ODkOHDpX+n9U5pUnTdU5pJCQk4MGDB6hSpYrsulGpnBo1auhVz7e5f/8+/vjjD5QvXx716tXLkzLfJQbMRP9xAQEB2Lp1Kw4ePCgNovDixQts27YN/fv3V7yTP3z4cJQsWRJNmjRB8eLFcevWLWzduhX79u3D6dOnUb58+Sy3qVar0b59e2zfvh21atVC//798fLlS2zevBkNGjTAn3/+iQYNGuTL/hJlV0REhM5gOXOeyMhI+Pn5vZM6EX1oIiMj8dNPP8Hd3R39+vVTzLN7925ZU92mTZtizZo1sLS0lNI0zU137twJBwcH7Ny5Ew0aNMCNGzfw9ddfY/369ShdujRmzZqVvzukB80TOhsbG8XlNjY2ePnyJdRqtVbgMnXqVGlALCMjI8yePRvDhw/XKiMkJAROTk4YOnSobECw3r17K36fve27ML/MmjULFy9exN9//60YpL1NYX86aWRkpDXQ1dChQzF79mysXbtWK2C+efOmXuW6urrKAuaszilN2vPnz7MsU5/zUp9ysmP16tXIyMiQBgH8r2HATPQf9/nnn6NYsWIICwuTAub169cjOTkZAQEBOHnypNY6R48e1RpF8cqVK6hduzamTp0q9W3RZfHixdi+fTu+++47TJo0SXoqN2HCBFSrVg0DBw7EpUuX3vq0LnMzu8yuX7+uePeU6LPPPpP1t9LHlStX9MrXtWtXWXPHrGRkZGSrDkQfspiYGHTo0AHm5uZYv369zieECxcuxMKFC/Hs2TOcOHECI0aMQK1atbB//364u7sDeH3DFnj9GVyyZAk+//xzAMBHH32EzZs3o2zZsvjpp58wefLkQvGUOacSEhKgVqtx584drFu3Dt999x3OnDmD3377TZZv9OjR+OGHHzBjxgx07doVZmZmOHDgAAYNGoR9+/bh77//LrC+vRpXrlzBxIkTMXr06BzNdx0fH4+9e/fC3d0dPj4++VDD3HN1dYWzs7MszdzcHNWqVcPBgwfx8OFDFC9eHMD/+p5/SMLCwqBSqRgwE1HBMDExQefOnREWFoaEhARYWVlh5cqVqFSpEmrUqKEYMCtNOVCxYkU0bNgQ4eHhb93m4sWL4ezsjJCQEFlQ7Orqir59+2L27Nm4ePEiqlSpkrudow+SiYmJdNPGxMRE1jfvzcGC8tL9+/dx//79fCuf6EMUGxuLxo0bIykpCXv27EH16tXfuo6dnR0+/fRTeHp6wt3dHUOHDsXOnTsBQBrky9DQUBrYSsPe3h7e3t7Yt28foqOjUa1atTzfn+zQPKnT1Rf0xYsXsLa21vnE1cDAAKVKlcKIESOQlpaGcePGoU2bNujQoQOA1328Z86ciREjRmDYsGHSeu3bt4cQAh07dsS8efMwefLkPN6z7Onfvz9Kly6N77//Pkfrr169Gunp6YU62NIEw2/SDMz14sULnXmyI/M5VbRoUdkyzXmmNBCerjKU6FuOvs6cOYMLFy6gUaNGKF26dJ6U+a4xYCZ6DwQEBOCnn37Cpk2b0KBBAxw+fBjTp0/Xmf/OnTuYMmUK/vzzT/z777+ygCTzSJtKEhMTcfnyZZQrVw6TJk3SWn758mUAr+8ovy1gvnTpkmK6rifP9GEwNjaWjYKa+fz08fHJ0RNmfQJhJyenbD1hzs/gneh9cPPmTTRs2BCPHz/Gjh07st1Vp3Tp0nB3d5cNjFWuXDkYGBjAwsJCGkQsM81FfnJycu4qnwc0T8WvX7+utUytViMuLu6tXaA0NIM8HTp0SBYwA4Cvr69Wfk3a2bNns13vvHbu3Dk8f/5c52jomhYHT58+hZ2dndby/8LTyYcPHyqmx8fHA5A3f85NH2Z3d3dERUXh+vXrWgGz5jzTnHe6WFlZwdHRETdu3FDsDqBvOfrKanTz/woGzETvgbp166JixYoICwtDXFwcVCoVunfvrpj30aNHqFOnDh48eIAmTZrA398fVlZWMDAwwNatW3Hu3Lkst/Xs2TMIIXDt2jWEhITozKfpe0WUl/bu3Svrz6iP8PBwnYMMZbZ27Vq9+zAnJibCysoqW/Ug+pDcvXsXjRs3xt27d7Fp0yY0bdo022UIIXD//n3ZWBxmZmbw9vbG0aNHcefOHbi4uMjW0UyzWBieZFWoUAEODg44cuQIUlJSZAHj8ePHkZCQgPr16+tV1r179wDIRxjX3ExUmkZIk/a2m+DvQkBAAJKSkrTSd+3ahfv376Nnz54wNDRUbEJ/7tw5nDt3Dg0bNoSrq+u7qG6O3Lx5E3fv3pU1y05OTsbZs2fh6Ogoe7qcmz7M9evXx7p163DgwAHUrl1blnf//v0wNDTUGhldSf369bFx40acPHkS3t7esjofOXIEH330kc4+ztmRlpaGNWvWwMrKCu3atct1eQWF00oRvSd69OiBiIgILFmyBI0bN9bqS6Px66+/4u7du1i5ciX27t2LuXPnYuLEiQgODkaJEiXeuh1ra2sAr6cvEELofBXWgTmo8EtPT8euXbuwa9cupKen57o8Pz+/twbCfn5+ik9piEi3tLQ0xMTE4NatW7L0hw8fokmTJoiLi8Pq1avh7++fZTl///23VpoQAhMnTsTTp0+lOYg1+vfvDwAYP368bB71tWvX4tKlS6hbt65WIJ3fHj16hJiYGNlASQYGBggMDMTz589lg5Clp6dLXZp69eolpf/777+Kwe+zZ88QHBwMALJj8fHHHwMAfvjhByQkJEjparUaU6dOBaD99NnPzw8qlSrf+tEqzfM8f/58LFu2TOuladGzePFiLFu2TLFv+3/l6aTmPc1s3rx5ePLkCbp16yZLj4uLy/L6KfPrzfepU6dOsLS0xPz586Wn18DrQfX2798Pf39/WZ/1pKQkxMTESDdcNDTnXVBQkGxMjlmzZuHFixfo06dPro6Hxp49e/Dw4UN07Ngx2ze7CxM+YSZ6T/To0QPjx4/H/fv3sxwdNDY2FgC0LmBSUlIUL1reZGNjgwoVKuD8+fNad8yJ8sKrV6/QsmVLAJBdBOaUSqXC5s2bdU4t5efnh82bN3NKKSIA06dPR0xMDABI//br10/qCjF79mzpgvzOnTvw9PSEr6+v7LPVoUMHREdHo2bNmrh8+bIU7Gm4ubnJAqDGjRujZMmSqFq1KkqWLImnT5/iyJEjuHTpEkqXLo2ZM2fK1u/Rowc2bNiA0NBQXL58GT4+Prhx4wa2bt0Ka2trLFmyJNfH4dGjRxgxYgSA/30PnThxQqq3h4cHxowZI+VfuHAhQkJCEBoaKtu3cePGYdu2bZgwYQKioqLg4eGB/fv3IyoqCt9++62sn3VUVBS6dOmC+vXro1y5crCzs8O///6LnTt34sWLF+jZsyc+/fRTKX+nTp2wYMECnDhxAh4eHmjVqhXMzMxw8OBBnD9/HlWqVNEakVxzg0HfubAPHz6MZcuWAfjfdEVhYWE4fvw4AKBNmzZo06aNlD8gIACRkZG4ceMG3Nzc9NqGLunp6Vi9ejWsrKzQvn37LPNm57zND1WqVMGuXbvg5+eHTz75BBcvXsSOHTvg6uqa477bSooXL45Zs2Zh0KBBqFGjBjp16oQXL15gzZo1KFasGObMmSPLf/LkSTRs2BCBgYFYsWKFlN6iRQt06tQJ69evR926ddGkSRNER0dj+/bt8Pb2/r/27jssiqttA/i9dJaugB07AhYkYi9gjb3EnhgxtlhiwR4TFYzGmmIsMRqjaOwmGo3R2IDYYu+CFewFuzSlPN8ffjsv6+7C0gTj/bsurpgzZ8+cMzNbnplTMGDAAK1yIiMjlaF+miFOmh4CwKvhUn379tWpb36f3dxoQkRvnaioKAEgXbt21UrfsWOHbNy4UeLj45W0H3/8UQDI0qVLRURkypQpAkC2b9+u5ElNTZXhw4cLAHn9Y2HSpEkCQKKiopS07777TgDIoEGDJCkpSSt/amqqhIeHZ6t9Xl5e4uXlla0y6O0VGxurXIuxsbE6/59VqampEhoaKn369JE2bdpInz59JDQ0VFJTU7NdR6K3jaHPWT8/P+Xa1veX9rtA813k5+enVUbJkiXTLeP1/N999500bdpUihYtKubm5mJjYyPe3t7y5ZdfyqNHj/TW/8WLFzJlyhQpX768WFhYSMGCBaVLly4SERFh9DEAIAEBAXq3adpmbBs035Wa79q0YmJipF+/flKoUCGxtLQULy8vmTdvns5nz7Vr12TYsGFStWpVKVCggJiZmYmzs7M0a9ZM1qxZo7eesbGxMnHiRPHy8hJLS0uxsLAQd3d3GTNmjDx9+lQnv7Ozs9SoUcOo4yMisnTp0nSPw6RJk7Tya66ftNeJIZq8CQkJerdv2bJFAEivXr2MLsuY61ZDc44NXQPG0lwPV65ckTZt2oidnZ3Y29tL586d5fr169kq25D169dL9erVxdraWpycnKRTp05y+fJlnXyhoaEG2/jy5UuZOnWqlCtXTiwsLKR48eISGBgoz58/N1iOoT995T98+FAsLCykdOnSWfqezU9UImn6shDRWyE6OhqlS5dG165dsWbNmnTzLly4EAMHDlTuel+7dg0VK1aEiKBr165wcHDA3r17cfXqVVSpUgXh4eFaXdyCgoIQHBysdbdYsw7zpk2bUL58efj5+cHJyQk3btzAoUOHcPv2bSQmJma5fZpJvwxNCkb/bWnHB8fGxr7V3biI8it+zr7qffL6k7f/sgsXLsDDwwNr1qxB165d87o6eU7zWyq714BKpdLpZUH/LRzDTPSOKVmyJPbs2YPq1atjw4YNCAkJQfHixXHw4EGju0+ZmJjgt99+w+LFi+Hs7Iy1a9di7ty5OHz4MHx9fbFy5crcbQQREVEO0MzAnJX1gd82+/fvh5ubmzLL9rtq06ZNUKlUepfYJNKHT5iJKN/hk493G58wE+U+fs5Ca2y1q6srBg0alHeVoTcmMjJSq3de1apVtcZhZxafMP/3cdIvIiIiInrnvD4ZGb0bPDw8eO4pUxgwExERERERZQE76/73MWAmIqJ8xcLCAvPmzVP+TURERJRXGDATEVG+Ym5ujsGDB+d1NYiIiIg4SzYRERERERGRPnzCTERE+UpKSgr27t0LAKhfvz5MTU3zuEZERET0rmLATERE+UpiYiIaNmwIgMtKERERUd5il2wiIiIiIiIiPRgwExEREREREenBgJmIiIiIiIhIDwbMRERERERERHowYCYiIiIiIiLSgwEzERERERERkR5cVoqIiPIVc3NzzJw5U/k3ERERUV5hwExERPmKhYUFRo8endfVICIiImKXbCIiIiIiIiJ9+ISZiIjylZSUFBw/fhwA8N5778HU1DSPa0RERETvKgbMRESUryQmJqJGjRoAgNjYWNjY2ORxjYiIiOhdxS7ZRERERERERHowYCYiIiIiIiLSgwEzERERERERkR4MmImIiIiIiIj0YMBMREREREREpAcDZiIiIiIiIiI9uKwUERHlK+bm5pg0aZLybyIiIqK8woCZiIjyFQsLCwQFBeV1NYiIiIjYJZuIiIiIiIhIHz5hJiKifCU1NRUREREAAE9PT5iY8N4uERER5Q0GzERElK8kJCSgUqVKAIDY2FjY2NjkcY2IiIjoXcXb9kRERERERER6MGAmIiIiIiIi0oMBMxEREREREZEeDJiJiIiIiIiI9GDATERERERERKQHA2YiIiIiIiIiPbisFBER5Svm5uYYNWqU8m8iIiKivMKAmYiI8hULCwvMmjUrr6tBRERExC7ZRERERERERPrwCTMREeUrqampuH79OgDAzc0NJia8t0tERER5gwEzERHlKwkJCShdujQAIDY2FjY2NnlcIyIiInpX8bY9ERERERERkR4MmImIiIiIiIj0YMBMREREREREpAcDZiIiIiIiIiI9GDATERERERER6cGAmYiIiIiIiEgPLitFRET5ipmZGQYNGqT8m4iIiCiv8JcIERHlK5aWlpg/f35eV4OIiIiIXbKJiIiIiIiI9GHATERE+YqIICYmBjExMRCRvK4OEf1HqVQq5a9SpUp5XR16QzZt2qR17nv16pXXVaJ8jgEzERHlK/Hx8XB1dYWrqyvi4+PzujpE75QffvgBH3/8MTw8PGBiYgKVSoXExMQslfXTTz+hSpUqsLa2RuHChdG3b1/cv39fJ9/evXsxfPhwVK1aFY6OjlCr1fDx8cE333yDpKQkg+WfO3cOH330EYoWLQpLS0sUL14c7du3x6lTp4yuo7e3NyZNmqTMm6Bx5coVTJw4ES1atICzszNUKhW6detmfOPTePDgAT799FMUKVIEVlZWqFSpEhYsWKD3huCWLVswaNAgVK9eHZaWllCpVNi+fbvBslNTU7F8+XLUrl0bzs7OcHBwgLe3N6ZNm4bY2Ngs1Tetf/75B4GBgahXrx5sbGygUqmwcOHCLJV16tQptG7dGk5OTrC1tUWDBg2wa9cunXyPHj3CkCFDUKNGDbi6usLS0hKlS5dGp06dcOzYsQz3k5KSgurVq0OlUqFWrVo62z08PDBp0iQMGzYsS+142yUnJ2PatGlwd3eHlZUV3NzcMHLkyExfL5m5rgEgOjoa3bt3h7OzM9RqNapXr461a9caLN/Y6wXI2c8tvYSIFF26dJEqVapIampqXlclTwUEBEhmPh5OnjwpAGTjxo05sn8vLy/x8vLKkbLo7RMbGysABIDExsbmdXWI/pMMfc5q3nslS5YUR0dHASAJCQmZLn/s2LECQNzd3WX06NHSvXt3MTU1lXLlysmjR4+08hYqVEjMzc2ladOmMmrUKBk8eLCULFlSAEjTpk0lJSVFp/yNGzeKhYWFODs7yyeffCKff/659O7dWypUqCArVqwwqo4AJCAgQO+2pUuXCgCxsLAQLy8vASBdu3bN9HF4/PixVKhQQQBI27ZtZcyYMVKtWjUBICNGjNDJ7+fnJwCkYMGCUqJECQEg27ZtM1h+nz59BICUK1dOhgwZIoGBgeLt7S0ApGbNmpKUlJTpOqel+T1gb28vZcqUEQDy448/Zrqco0ePilqtFmtra+nTp48EBgZKsWLFxMTERDZt2qSVNyIiQmxtbaVZs2YycOBAGTdunHTv3l3UarWYmJjIunXr0t3XjBkzxMbGRjkGhkRFRaV7DfxXde3aVQBItWrVZMyYMdK2bVsBILVr15YXL14YVUZmr+vo6GhxdXUVU1NT6d69u4wePVrc3d0FgPzwww86+TNzvYjk3OeWIQyY6Y2pVKmSAJBu3brldVX0Onr0qKhUKlm7dm1eVyXP6QuYJ02aJAAkNDRU72tat24tXl5ekpycnO39M2B+tzFgJsp9hj5n//rrL4mJiRGR/wVvmf3heebMGTExMZHKlStLXFyckh4SEiIAJDAwUCv/zJkz5d69e1pp8fHxUrNmTQEgq1ev1tp2+fJlUavVUrduXXny5InO/o0NEtMLlq5evSqHDh2SxMREJbDKSsA8evRoASCTJ0/Wql/Tpk1FpVLJyZMntfLv3btXrly5IiL/+941FDBr6uXj46MV6KSmpkqLFi0yDLaNceTIEYmIiJDU1FTlJkJWAuYaNWqIiYmJhIWFKWn37t2TwoULS9GiRbWusaSkJL3nMCIiQqysrKRMmTIG93PhwgWxsrKSb7/99p0PmOPj4+X06dNaaVu3bhUA0qxZM63fa8HBwQJA5syZY1TZmb2uO3fuLABk+fLlSlpsbKxUrlxZ1Gq13LlzRyt/Zq4XkZz53EoPA2Z6I44ePSoARKVSiZWVld4vuLzWrl07KV68uN472e+a27dvS0REhFZaRgHz7t27BUCO3HBgwPxuSxswv/7HAJooZxjzOZvVH56jRo3SG+iKiLi7u0vBggWNCmpXr14tAGTw4MFa6b179xYTExMlsMwqY4OlrAbMKSkp4urqKg4ODjrHcP/+/QJAhg4davD1GQXMoaGhAkBGjhyps+2HH34QADpP269duyYRERHy8uXLTLVFRLIcMJ89e1YAyPvvv6+zberUqQJAfv/9d6PK8vHxERMTE709AVNTU6V+/fri6+srycnJbyxgBiB+fn5y5coVadOmjdjb24u9vb107txZbty4ka2ys+LgwYPSv39/sbe312lbp06dBIAcPHhQKz0+Pl7s7e2lSpUqGZaf2ev68ePHYmZmJhUqVNApa+XKlQJAvv32WyUtu9dLbgTMHMNMb8SyZcsAAMOGDUNiYiLWrVuXtxV6ze3bt/Hnn3+iS5cuMDHh26JIkSLw8PDI1Gv8/f1RuHBh/Pzzz7lUKyIgPDycE4ER5XN79+4FADRu3FhnW+PGjfHw4UOcP38+w3LMzc0BaK/HLiL4/fff4ePjgzJlyiA0NBQzZ87EnDlzjBrf+iZdvHgR9+/fR926dWFlZaW1rVatWrC1tVWOVVa4u7vD1NQUe/bswcuXL5V0EcH27dthbm6OOnXqaL2mZ8+e8PT0xK1bt7K838xK73po0qSJVp70REdH4+LFi/D09IRKpdLZvmDBAhw8eBCLFy+GqalpNmudOY8ePUKDBg3w7NkzDBo0CH5+fli/fj3q16+PR48e5fr+79y5g5kzZ8LT0xO1a9fG4sWL4ePjg+7du2vl27t3L2xtbVGjRg2tdGtra9StWxenT5/Gs2fP0t1XZq/rf//9F8nJyUaf/5y6XnISIwPKdS9fvsTq1avh5eWF4OBgWFlZISQkxGD+O3fuoEePHihQoADs7OzQpEkTnDhxAr169dL7ARkfH4/JkyfDy8sLVlZWKFiwIDp37oyLFy8aXcdVq1YhJSUFH3zwgc42zX4vX76MiRMnws3NDVZWVnjvvffwxx9/6C3vxo0b6NevH0qUKAFLS0u4ubkhMDAQT58+1coXFhYGlUqFoKAg7N69G/Xr14etrS38/f3TrW9QUBBUKhWio6MN1ldf/rCwMCxevBienp6wtLSEu7s7fvnllwzL8Pf3R3BwMACgYcOGemeWNDExQbt27bBr1y7cuXMn3foTZVWrVq3QqFEjPH78OK+rQkQGXL58GXZ2dnBxcdHZVrZsWSVPRpYvXw4AaNSokZJ25coVPHnyBMWLF0fr1q3RqFEjjB07FsOHD4evry+6du2KFy9e5FBLskfTRk2b0zIxMUGpUqWMOg6GFC1aFF988QVOnDiBihUrYtiwYRg5ciR8fHxw4MABLFu2DGXKlMly+TklveOQ3vVw+/ZtBAUFYcKECejVqxeqVq0KlUqFuXPn6uS9du0axo0bhxEjRqBq1ao52wAjnDlzBq1atUJYWBimTZuGzZs34+uvv0Z0dDSmTJmilff7779HUFCQUX/ff/+9wX2+fPkSv/32G1q3bo0SJUpg7NixePnyJYKCgnD16lWEhYXh/fffV/LHxsbi3r17KF26tN6HQ8a+NzN7XaeX39XVFba2tkbnz8znR04yyzgLUfb8+eefePjwIYYPHw57e3u0bt0aGzZswOXLl1GuXDmtvE+fPkWDBg1w+fJlNG/eHD4+Pjh9+jT8/Pz0fgDGx8ejYcOGOHz4MPz8/NCiRQvcv38f69evx65du3Dw4EGjnpSGhYXBzMwM7733nsE8Q4cOxalTp9ClSxflJkCHDh2wfv16dOzYUckXEREBPz8/PHnyBO3atUOpUqVw9uxZfP/99wgPD8eBAwd07sjt3bsXX3/9NVq0aIHBgwfDwsIiwzpnxXfffYd//vkH7du3R9OmTbF+/Xr06dMHzs7OaNu2rcHXaQLj8PBwBAQEoFSpUgCgc05q1aqFn376CeHh4VmeTZQooyfIYWFh+OCDD7Bnzx69N9GIKG89e/YMrq6uerfZ29sDgM4N5NetXLkSmzdvRr169dCmTRsl/cGDBwBe/bZwdXXFn3/+iQYNGiAqKgqfffYZ1q1bBzc3N8yaNSuHWpN1mid1mja/zt7eHs+fP0dqamqWe7cFBwejcOHCGD58OH744QclvXfv3npvvoeFhWVpP9mR3nFI73q4ffu2crMeAFxcXLBy5Uo0bNhQJ++nn36KQoUKISgoKIdqnTlmZmaYNGmSVtrw4cMxe/ZsrF69Gt9++62S/v333+PatWtGlVuyZEkMHz5cK+3kyZNYunQpVq5ciYcPH8LOzg49e/ZEr169UL9+fYPfi8Zcj0DG783MXtfG5E+7z6xeL7mJATPlOs3T5A8//FD574YNG7B8+XJMnjxZK+/06dOVJ7lpPySnTZuG8ePH65Q9adIkHD58GIsWLUK/fv2U9OHDh6N27doYPnx4ussxaPz777/w8PCAtbW1wTynT5/GqVOn4OzsDAAIDAyEt7c3PvvsM7Rp00YJcnv27Im4uDgcPnxYK6D84YcfMGzYMHz//fcYN26cVtl79uzBunXr0Llz5wzrmh0HDhzAiRMnlIA3MDAQFSpUwA8//JBhwBwdHY3w8HD06tXL4BPwatWqKfthwExZtX///gzzhIWFITw8PMPeGET09tm7dy/69euHokWLYtWqVVoBQGpqKoBXSwctXLgQrVq1AgBUqVIFv//+O8qUKYMFCxZgypQpsLS0zJP6v0ljx47Fd999hxkzZqB79+6wsrLC7t27MWjQIOzcuRPHjx9Xfre8bXx9fSEiePnyJa5cuYJvvvkGLVq0wLx58zBgwAAlX0hICP7++2/s3Lkz3d9xualkyZIoWrSoVpq1tTWqVq2KPXv2ICYmRulxoa93oLGuXbsGHx8fAEDNmjXx3XffoWPHjlCr1VkukzLGLtmUq2JiYrBt2zbUrFlT6RbUsmVLODo6Yvny5TpPklatWgV7e3uMHj1aK3348OEoWLCgVlpKSgoWL16MmjVragXLwKvArV27dtixY0eGd6ESExPx8OFDg3fDNYYOHar1pVOuXDkEBATg7t27CA0NBQAcPXoUR48exZAhQ3Sevn722WdwdXXF+vXrdcquUaNGrgfLwKs2aIJlAChdujTq1auHEydO5Ej5hQoVAgDcvHnTqPwVK1bU+3flypUcqQ+9nfS9R/T59ddfc7kmRJQV9vb2BsdBatIdHBz0bj9y5Ahat24NW1tb7Nq1CyVKlNDarnmdqakpWrRoobXN2dkZtWrVQnx8PCIiIrLbjGzTPA1L71jY2dll+eny33//jZkzZ2LYsGEIDAxE4cKF4ejoiI4dO2L+/Pm4ceNGul1635T0jkNG1wMAWFhYwNPTEz///DOaNGmC4cOHK2OwHz16hMDAQPTs2VMZ35oX9A0/AKD8tsxoXLCx1Gq18jvu9OnT2L59O/bt26fcSEqPMdcjkP65MLactNe1MfnT7jO710tu4BNmylUrV65EUlKS8nQZACwtLdGxY0csWbIEYWFhSteap0+f4vr166hbty5sbW21yrG2toa3tzf27NmjpF24cAFPnz5FSkqK3i44t2/fhojg0qVL8PX1NVhHzWQMTk5O6bbl9YkzAKB27dr48ccfcfr0abz//vs4fPgwgFdjrPTVyczMDBcuXNBJT68reE7y9vbWSStatCjCw8NzpHzNMXz48GGOlEfvpvv37+doPiJ6s8qVK4dDhw5pPVXT0NwQfX1IFgCcOHEC77//PkxNTbFz5054enrq5ClbtixMTEygVquVScHS0vyQTkhIyImmZIumjfpuAqempiI6Ohrly5fPcvl///03AMDPz09nmybt5MmTWS4/p6R3HNK7HvRp1KgR/v77bxw+fBgdOnTA9evX8fjxYyxfvlwZ857WoUOHoFKp4O3tnavHIiYmRm+65nsqbffi77//Hk+ePDGqXEdHR60u2S4uLrh69SpCQ0OxdOlS/P7771i1ahWKFi2KHj16ICAgAF5eXnrLsrW1RaFChRAVFaV3GICx5yKz13V6+e/fv4/Y2Fitfebk9ZJTGDBTrgoJCYGpqSm6du2qlf7RRx9hyZIlCAkJUQLm58+fA8j4Lp2GZtIfzVNdQ+Li4tKto6b7TmJiYrr59NXr9TuHmjpt2LABGzZsSLc8feXkNn3jQczMzIy6M2kMzQ8UY7tEnTt3Tm96xYoVc6Q+9HYy9Bnwujf1viGizKlfvz4OHTqE3bt36wzP2b17NwoWLKjzo/7cuXNo1qwZkpOTsWvXLr03eAHAysoKtWrVwoEDB3Dr1i0UK1ZMa7vmybKbm1sOtihr3N3d4erqiv379yMxMVFr/pJ///0XsbGxqF+/fpbL18yMrRnXnZYmLbfmRMkMTRt3796t04Nw165dWnkyoplUVDNzesGCBdGnTx+9eZcsWQIXFxe0bds216+Ha9eu4fbt21rdshMSEnDy5EkUKlRI63stu2OYVSoVGjVqhEaNGmH+/PlYu3Ytli5dipkzZ2LmzJnw9fVFQEAAunfvrtM7s379+tiwYQMOHz6MWrVqadV1//79qFKlisGxxhqZva5r1aoFMzMz7N69W6csfec/J6+XnMIu2ZRrTp8+jZMnTyIlJQWFCxdWZlbWvNEB4LffflMCWjs7OwAZ36XT0OTv27cv5NWa4nr/9N15TcvR0RFmZmYZTvuvr16v3znU1OnXX39Nt06vy+zERZq7gsnJyTrbcqrbT1ZobhgYG/AQ6ZN2Er309OjRI5drQkTpSUpKQmRkJK5fv66VHhAQABMTE3z99deIj49X0pcvX46LFy+iZ8+eWktFXbp0CU2aNEF8fDy2bt2qs+TN6/r37w8AmDBhgtZ36urVq3Hu3DnUrFlTJ5DObQ8ePEBkZKTWMDATExMEBATg6dOnWpOQJScnIzg4GCqVCp988kmW91m7dm0Aryb0jI2NVdJTU1Px9ddfA9B9+uzv729wlY2ccP36dURGRiIpKUlJq1ixIqpXr46dO3dq9Wi7f/8+5s6di6JFi2p1rz937pzWMlkap0+fxi+//AK1Wo169eoBAEqUKIGff/5Z7x8AlClTBj///DMmTpyYK+3V0JzTtL7//ns8evRIq5cl8GoMc3q/EdP+ZXSe7O3t0a9fPxw4cACRkZEYO3Ysbt++jSFDhqBIkSKYPn26Vn7N9TZp0iSkpKQo6bNmzcKzZ890bj7kxHXt6OiIDh064MKFC1ixYoWSHhcXh+nTp0OtVmstf5XZ6+VN4BNmyjWatZebN2+u94vr/PnzOHjwIH777Tf07NkTDg4OcHNzw9mzZxEXFwcbGxslb2JiIk6dOqX1ek9PT9ja2uLIkSMQkSzPlqtSqeDl5ZXhFPUHDhxQPqA1Dh48CACoXLkyAKB69eoAXnUB+uijj7JUH2M4OjoCeNXtPG23lNTUVJ3jlFM0QXp6T6M1S3lpjgdRVjRo0CDDPP7+/hneDCOizJs+fToiIyMBQPlvv379lHVtZ8+erczncevWLXh6esLPz09r9uVKlSph1KhRmDlzJnx8fNCuXTvcunULa9euRbly5TBhwgStfTZp0gR3795F48aNsXv3bp0nUVWrVkX79u2V///444+xfv16LF26FOfPn0e9evUQFRWFTZs2wc7ODgsXLsz2cXjw4AFGjRoFAEoweujQIWXVCA8PD60JPOfNm4fg4GAsXbpUa8nF8ePH448//sDEiRNx7NgxeHh4YNeuXTh27JjeJZA2bdqETZs2Afhfd+pvvvkGa9asAfDqIYHmt0iXLl0wd+5cHDp0CB4eHmjTpg2srKywZ88enD59GpUrV9aZ40VzgyHtDYv07Nu3Twk+Nb+TQkJC8O+//wIA2rdvr3VuevbsifDwcERFRWnNmfLjjz+iQYMGaNmyJbp37w57e3usW7cO9+7dw8aNG7WeUi5evBi//vor6tWrh1KlSsHU1BQXLlzA9u3bISL4+eefMxxG96ZVrlwZW7duhb+/P+rUqYOzZ89iy5YtKFmyJL788ss3UocKFSpg+vTpmDp1Kv7++28sXboUUVFRWnlatmyJLl26YN26dahZsyaaNGmCiIgIbN68GbVq1dKaTA3Iuet61qxZCA8PxyeffILt27ejWLFi+OOPP3Dx4kX88MMPKFy4sFb+zFwvQOY+t7JEiHJBUlKSuLq6ilqtlmfPnunNc+bMGQEgjRo1UtLGjh0rACQ4OFgr7/Tp0wWAvH7JDhs2TADI119/LampqTp12Ldvn1H1HThwoACQ6OhonW0BAQECQIoVKyYxMTFK+qVLl0StVkuhQoXkxYsXIiKSmpoqVatWFXNzcwkNDdUp6+nTp3LixAnl/0NDQwWATJo0yah6auzbt08ASJ8+fbTSv/vuO73HadKkSQJAb5007csobe7cuQJAli9fbrBeU6ZMEQBy4MCBTLXndV5eXuLl5ZWtMujtFRsbq1zH+v78/f3l0aNHeV1Noreaoc9ZPz+/dN9/UVFRSt6oqCgBIH5+fjrlpKamysKFC6VSpUpiaWkprq6u0rt3b7l3755O3vT2B0ACAgJ0XvPixQuZMmWKlC9fXiwsLKRgwYLSpUsXiYiIMPoYGCo7bdsM/b3eZs337NKlS3XKiomJkX79+kmhQoXE0tJSvLy8ZN68eTq/W9KWY+jv9fJjY2Nl4sSJ4uXlJZaWlmJhYSHu7u4yZswYefr0qU75zs7OUqNGDWMPkSxdujTd+rz++0Vz/aS9TjROnjwpLVu2FAcHB7GxsZF69erJzp07dfLt3btXevbsKeXLlxdbW1uxsLAQNzc36d69uxw6dMjougOQmjVrGtyuOceGroHM7MfPz0+uXLkibdq0ETs7O7G3t5fOnTvL9evXs1V2diUnJ+ukvXz5UqZOnSrlypUTCwsLKV68uAQGBsrz58918ubUdS0icvXqVenatasUKFBArKyspFq1arJ69WqDdTf2ehHJ3OdWVjBgplyxefNmASAffvhhuvl8fHxEpVLJtWvXRETk8ePHUqZMGQEgLVu2lM8//1z58Klfv76oVCqt18fFxUmtWrUEgHh7e8uAAQNk5MiR0qlTJylcuLBUqFDBqPpu27ZNAMgvv/yis00TPLZo0UKKFi0qw4cPl0GDBomTk5OoVCpZv369Vv7z589LoUKFRKVSSdOmTSUwMFA+++wzadmypajVavn000+VvFkNmFNTU6V69eoCQN5//30ZNWqUNG7cWJycnKRBgwa5EjCfPn1aAEjx4sVl7Nix8tVXX8nmzZu18jRq1EgKFSokSUlJmWrP6xgwv9vSC5j/+usvg1/GRGQ8fs6mHzD/F0VGRgoAWbNmTV5XJV/I6YCZ/rs4hplyhWbt5Z49e6abLyAgACKizGzo6OiIf/75B927d8eBAwcwd+5cxMXFITw8HA4ODsoYYQ21Wo2wsDDMmjULJiYmWL58ORYuXKjMWj1//nyj6tusWTO4ubkpXZ70+eGHH9CnTx9s2LABS5YsQcmSJbFx40Z06tRJK5+npydOnjyJoUOH4urVq5g/fz5WrVqFW7duYdCgQRg2bJhRdUqPSqXCli1b0KVLFxw8eBALFy6EhYUFDhw4gNKlS2e7fH0qV66MhQsXwsbGBt9++y0mTJiA3377Tdl+9+5dhIWFISAgwOiuXkQZiY2N1RrP1aJFiywPvyAiel1ISAhUKhUqVaqU11XJdfv374ebm5vO75Z3zaZNm6BSqXLt9xL996hE9MxARJTPpKamomzZsnB2dsaRI0dyZR+zZs3C+PHjcfXqVa11H3v16oWQkBCd8TikbebMmfjyyy9x4cKFbH8JaWbJNjSLNv23xcXFKUvLxcbGas1nQEQ5g5+z0Fr+0dXVFYMGDcq7ytAbExkZqfWA5PUx8pmlUql0xvHTfwsfA1G+c/fuXZ3B/99++y2io6OzNZtkRj777DPMmTMHM2bMwLx583JtP/9FiYmJ+Pbbb9G/f3/esSUiordC2oCZ3h0eHh4895QpDJgp32nQoAGKFSuGypUrw8TEBEeOHMGBAwdQtmxZnbXocpK1tTVCQkJw9OjRbM26/S66fv06BgwYgIEDB+Z1Veg/wNTUVOkyqJnhkoiIKD9iZ93/PnbJpnxn2rRpWLt2LaKjoxEfH48iRYqgdevWmDRpElxdXd94fdgl+81jV0EiotzFz1kiIuMwYCaifIc/5IiIchc/Z4mIjMNZsomIiIiIiIj0YMBMRET5SlxcHFQqFVQqFeLi4vK6OkRERPQOY8BMREREREREpAcDZiIiIiIiIiI9GDATERERERER6cGAmYiIiIiIiEgPBsxEREREREREejBgJiIiIiIiItLDLK8rQERElJapqSlatmyp/JuIiIgorzBgJiKifMXKygpbt27N62oQERERsUs2ERERERERkT4MmImIiIiIiIj0YMBMRET5SlxcHGxsbGBjY4O4uLi8rg4RERG9wziGmYiI8p34+Pi8rgIRERERnzATERERERER6cOAmYiIiIiIiEgPBsxEREREREREejBgJiIiIiIiItKDATMRERERERGRHpwlm4iI8hUTExP4+fkp/yYiIiLKKwyYiYgoX7G2tkZYWFheV4OIiIiIXbKJiIiIiIiI9GHATERERERERKQHA2YiIspX4uLi4OLiAhcXF8TFxeV1dYiIiOgdxjHMRESU7zx48CCvq0BERETEJ8xERERERERE+jBgJiIiIiIiItKDATMRERERERGRHgyYiYiIiIiIiPRgwExERERERESkB2fJJiKifMXExAS+vr7Kv4mIiIjyCgNmIiLKV6ytrXHkyJG8rgYRERERu2QTERERERER6cOAmYiIiIiIiEgPBsxERJSvxMfHo1SpUihVqhTi4+PzujpERET0DuMYZiIiyldEBNeuXVP+TURERJRX+ISZiIiIiIiISA8GzERERERERER6MGAmIiIiIiIi0oMBMxEREREREZEeDJiJiIiIiIiI9OAs2URElK+oVCp4eXkp/yYiIiLKKwyYiYgoX1Gr1Th37lxeV4OIiIiIXbKJiIiIiIiI9GHATERERERERKQHA2YiIspX4uPjUbFiRVSsWBHx8fF5XR0iIiJ6hzFgJiKifEVEcP78eZw/fx4iktfVIaL/KJVKpfxVqlQpr6tDb8j333+vde6DgoLyukqUzzFgJiIiIiJFWFgYGjZsCDs7Ozg6OqJFixY4ceJEpl6fNiB5/S8xMVEr/+7du9GtWze4u7vD3t4ednZ2qFy5MiZMmIDHjx/rlH/y5EmMGzcOjRs3hoODA1QqFcaNG5eltnp7e2PSpEkYNGiQzrbk5GRMmzYN7u7usLKygpubG0aOHInY2Fijy1+4cCFatmwJNzc3WFtbw8XFBbVq1cKSJUuQnJyslTc2NhbLly9Hx44dUbZsWVhZWcHZ2RmtW7fGP//8o7f8pKQkTJ8+HV5eXrC0tISzszM6d+6MixcvZu5ApOPBgwf49NNPUaRIEVhZWaFSpUpYsGBBpm5opqamYvny5ahduzacnZ3h4OAAb29vTJs2Te/xTO/62b59u1beXr16pZtfpVLhxo0bSv5atWph0qRJCAgIyPpByWM//fQTqlSpAmtraxQuXBh9+/bF/fv3M1VGXFwcxowZg5IlS8LKygrlypXDlClTkJSUpDd/Zq4DEcGiRYvw3nvvwdraWvkcOXz4sMH6iAiWL1+O+vXrw8HBAba2tqhYsaLe9+ab9p+fJTs6OhqlS5fGpEmT3so7SJovrbep/kFBQQgODkZUVBRKlSoFAFi2bBk++eQThIaGwt/fP0/rpzF27FiEhIQgKioK1tbWeV0do+TX63nEiBFYu3YtLl++/NYcSyIi0rV161a0bdsWDg4OSkCxatUq1K1bF+Hh4ahevbrRZfn5+en9zjcz0/75uWvXLhw6dAi1atVCsWLFkJqaiiNHjmDKlCn49ddfcfToURQsWFDJv2nTJsyYMQPW1tYoUaIEnj17lrXGAqhatarB79MePXpg7dq1qFatGoYNG4bIyEh8++23OHjwIMLCwmBhYZFh+StWrEB8fDwaN26MQoUKITY2Fn///Tf69u2LzZs3448//lDy7tu3DwEBAShUqBCaNGmCEiVKICoqCr///jv++usvLFu2DD179lTyp6amon379vjrr7/g4eGBgQMH4smTJ1i3bh127tyJAwcOKEv0ZdWTJ09Qr149XLhwAW3btoWHhwd2796NwYMH48qVK/jmm2+MKqd///5YsmQJypUrhw8//BBmZmbYs2cPxo8fjz/++AP79u3TuS5KliyJXr166ZRVrlw5rf9v37698nszrbt37+Knn36Cu7s7SpQooaTXqlULtWrVQlhYGEJCQoyqf34ybtw4zJgxA+7u7hgyZAhu3ryJZcuWITw8HIcPH4aTk1OGZSQlJaFFixbYu3cvGjZsiO7du+PQoUOYMGECTp48iQ0bNmjlz+x1MGjQICxcuBAlSpRAnz598PLlS/z222+oV68e/v77bzRs2FArf0pKCj7++GOsXr0aPj4+6NOnD0xNTXH16lWsW7cOCxYsyP6Byw7JQU+fPpUvvvhCvLy8xMrKSqytraVUqVLSsmVLmTlzprx48ULJu3TpUgEgS5cuzfZ+J02aJAAkNDRUZ1tUVJQAkEmTJmV7P9kVEBAgACQqKsro14SGhuab+htLcz7StlNzvvWdo7xw8+ZNsba2lhkzZuR42bGxsTJ69Ghxc3MTS0tLKVu2rHz11Vfy8uXLbJedn67ntG7duiWWlpYybdq0HCnPy8tLvLy8cqQsevvExsYKAAEgsbGxeV0dov8kfZ+zL168kOLFi4tarZbIyEglPSIiQtRqtbz33ntGlZ3Z3y4JCQl604OCggSATJ48WSv97NmzcvLkSUlKSlL2NXbsWKP2lRYACQgI0Ltt69atAkCaNWsmycnJSnpwcLAAkDlz5hi1D31tS05OlsaNGwsA+eeff5T0kydPyurVqyUpKUkr/8GDB8XCwkIcHBwkMTFRSV+9erUAEH9/f630EydOiLm5uTRo0MCoOqZn9OjROucgKSlJmjZtKiqVSk6ePJlhGZrfLj4+PlqxQGpqqrRo0UIAyLZt27ReA0D8/PyyVffp06cLAJk6dare7W/jb+wzZ86IiYmJVK5cWeLi4pT0kJAQASCBgYFGlTN//nwBIH369NFK7927twCQP/74Qys9M9fBwYMHBYB4eHjIkydPlPQbN25IgQIFpGzZsjrX+LRp0wSAzJ49W6eur+fNCznWJfvJkyeoWbMmpk6dCpVKhU8++QSBgYGoX78+zp49izFjxmTrDmBWFStWDBEREfjss8/e+L7pfzp06ICIiAjUqFEjr6sCAJg1axZEBAMGDMjRcjV37GbNmoWyZcti+PDhKFGiBCZMmIDu3bvn6L7yk6JFi6JLly6YOXOmTle73BIXF6d0tYqLi3sj+6SM8bwQvb127tyJmzdv4uOPP0aFChWUdA8PD3z00Uc4fvx4prpmG8vKykpveocOHQAAV69e1UqvWLEivL29dZ5I5qSlS5cCAIKDg2Fqaqqkjx49Gvb29liyZIlR5ehrm6mpKdq2bQtAu23e3t7o1q2bTrtq1aqFhg0b4unTpzhz5oySvmXLFgCvesxZWloq6VWrVkWHDh3wzz//6HTNvn79OiIjIw12u00rNTUVISEhcHBwwOjRo5V0MzMzBAUFQUTwyy+/ZFhOdHQ0AKBRo0ZaT+VVKhVatGgB4FV335wWEhICExMTrafyOS06OhoqlQq9evXCsWPH0KhRI9ja2sLZ2Rn9+/fXO6QgO0JCQpCamorx48dDrVYr6T179oS7uzuWL1+u09Vfn6VLl0KlUmHy5Mla6ZMnT4ZKpdK6vjN7HWiuy2HDhsHBwUFJL168OPr27YsrV65gz549SnpcXBymTZsGf39/jBw5Uqeuufk+N1aOBczff/89IiMjMWjQIJw9exYLFizA1KlTsXz5cly7dg3h4eGwsbHJqd0ZzdzcHB4eHnB2dn7j+6b/cXBwgIeHh9abO68kJiZi+fLlaNmyJezt7bNVlrw2bmPx4sXYu3cv+vTpgz179mD69OkIDQ1F79698dtvv2Hz5s3Z2l9+1q1bNzx+/Bi///57XlflrSIiCA0NRd++fdG2bVv07dsXoaGhnOyKiN64vXv3AgAaN26ss61JkyZaeYxx8eJFfPfdd5gxYwY2bNiQ6Qcnf//9N4BXAfKbtnfvXtja2urc6Le2tkbdunVx+vTpLD8IEhHs3LkTgPFtMzc3B6AdPGjGrJYsWVInv6aLcnh4uFZ6z5494enpiVu3bmW4z4sXL+L+/fuoW7euTuBfq1Yt2NraGnU9uLu7w9TUFHv27MHLly+VdBHB9u3bYW5ujjp16ui87vHjx1i4cCG+/vprLF26VGscckaOHDmCiIgINGrUCMWLFzf6dVl15coVNGzYELa2thgyZAgqVaqExYsXo1mzZlptzq703qONGzfGw4cPcf78+XTLiI+Px7Fjx+Dp6YmiRYtqbStWrBg8PDy0zmtmr4PMXpc7duzAs2fP0LFjRzx79gwrVqzAtGnTEBISkulx2bklxwJmzSDu/v37693eoEEDZWxjr1698MknnwAAPvnkE+VpRNpxLqtWrUL79u1RsmRJWFpawtXVFZ06ddK5CPz9/REcHAwAaNiwoVKWZsyD5s7P6+NTNPu7efMmOnfuDEdHR9jZ2aFjx464c+eOUW1OTU3FggUL0Lx5cxQvXhwWFhYoVqwYPvnkE9y8eVMrb6lSpZRxEqVLl87SzHw7d+5EzZo1oVarUbRoUYwZM0bnaV5MTAwmT56MWrVqwcXFBZaWlnB3d8eECRP0Pvk7f/48unXrBjc3N1haWqJQoUKoX78+li1bppP34MGDaNu2LQoWLKgM9p8zZ45RP+yXLVsGlUqFsLAwJU0zKUhQUBD27t2LevXqQa1Wo3Dhwhg5cqTeu5/Jycn44Ycf4OPjA7VaDQcHhwwnEXjdn3/+icePH+ODDz4w+jUaIoLjx49jypQpqFOnDqZPn661PTN37DKyatUqVKlSBVZWVihZsiSCg4ORkpJiMK8x7xdND5A///xTbzmdOnWCiYmJcjc4JSUF8+bNg4+PjzIBQ9myZdGrVy+d90mzZs1ga2uLFStWGN3Gd93jx4/RqFEjNGrUCEuWLMGWLVuwZMkSJS2n70y/LVQqFUqWLImSJUtCpVLldXWI3hmXL18GAJQtW1ZnmyZNk8cYq1evxogRIzBu3Dh07twZJUuWxNq1aw3m/+uvvxAUFITRo0ejadOmGDt2LGrUqIGBAwdmsiXZExsbi3v37qF06dIwMdH9qZyVY7FgwQIEBQVh2LBhqFKlCv78808MGjQIvr6+Gb729u3b2L17N1xdXbVm89aM67527ZrOazTf45cuXTK6jq9L73owMTFBqVKljDoGRYsWxRdffIETJ06gYsWKGDZsGEaOHAkfHx8cOHAAy5YtQ5kyZXRed/r0aQwcOBBffPEFevfujTJlymDMmDFG/e7U/ObWNwY6N+zbtw+ff/45Nm/ejGnTpiEsLAz9+/fH0aNHsXDhQq28QUFBRv+9/nv88uXLsLOzg4uLi04djL0ur169ChHRe1415Tx+/BiPHj3SKs/Y6yCz1+WxY8cAvPpNVKFCBfTs2RPjx49Hr169UKZMGaxcuTLd9rwROdW3+6OPPhIAsn79+gzzbty4Udq1aycApF27djJp0iSZNGmS1njmwoULi6+vr/Tu3VvGjh0r3bt3F2tra7G3t5eLFy8q+ZYuXSp+fn7KOBRNWRs3bhQRw2M+AUiVKlWkVKlSUqdOHRkxYoQyjqJatWqSmpqaYTsSEhJEpVJJ/fr1pX///jJmzBjp0KGDmJmZSfHixeXBgwdK3u+++068vb0FgAwbNkypZ0ZjejXjK5o2bSqWlpbSvXt3GTt2rPj4+AgAadu2rVb+bdu2ibW1tbRu3VqGDh0qgYGBUrduXQEgLVq00Mp748YNsbOzExsbG/noo4/k888/l379+km1atWkefPmWnnXrFkjpqam4uzsLL169ZLAwEClPQMGDNDKa+wYZk3bmjdvLlZWVtKhQwcZMWKEUu7r4zBSUlKkbdu2AkB8fX1l2LBh0rt3b3F0dBRLS0sJDw9P91hqDB48WADIpUuXjMr/9OlT2bBhg/Tu3VuKFCmijK0sU6aMcp2JiMTFxYlKpTI49tbT01OcnJyM2udPP/0kAMTV1VWGDBkiQ4cOlcKFCyvX6OvXs7Hvl5s3b4qpqal07NhRZ58PHjwQCwsLadSokZIWGBiojDsKDAyUUaNGSceOHcXBwUEOHjyoU4afn5/Y2Nhke7yJMWOY3/ZxrqmpqeLv76+0Qd+fv7+/UZ9F+cnbfl6I3hX6PmebNm1q8Pvx4sWLAkB69uyZYdlnz56V2bNnS2RkpMTHx8v169dlwYIF4ujoKKamprJ37169r9N8P2v+mjZtKjExMenuKzfGMN+6dUsASN26dfW+bvz48QJA9uzZY/S+KlasqNW2kSNHGvVdmZKSIu+//74AkMWLF2ttW7JkiQCQRo0aaY0NPnXqlFhYWAgA6d+/v9F1fN3KlSsFgHzxxRd6t9epU0cASEpKilHlLViwQKmX5q93795y69YtnbyjR4+WI0eOyJMnT+TBgweyZcsWqVChggCQr7/+Ot39vHjxQgoUKCD29vZa43xflxNjmDVxRoECBSQ+Pl5r282bN8XMzExq1aqllZ7e9/7rf6+P4zY3N5dixYrprcuiRYsEgPzyyy/p1nn//v0CQD766CO92z/88EMBIFevXhWRzF8Hu3fvFgDi6ekpz549U/LdunVLChYsqMwNoPHpp58KADE1NZXWrVtLZGSkPHnyRNasWSOOjo5iZmYmJ06cSLdNuS3HAubff/9dAIitra2MGjVKdu3aJc+fPzeYP6NJvzQnKa3IyEixs7OTXr16aaVnZdIvzYU4evRorfQ+ffoIAIMf5mmlpKTItWvXdNL/+ecfMTU1laCgIK307Ez6BUBWrVqlte/mzZvrDMx/+PChPH36VKecr7/+WgBIWFiYkjZnzhy9A/tFRCvYv3v3rtjY2EilSpXk4cOHSnpycrJ06tRJAGgFT5kNmAHIn3/+qaQnJiaKl5eX2NjYaE1iMW/ePOUNmzaIiI6OFkdHR/H09DQquKhWrZrY2dmlm+f8+fMya9YsadiwoZibmwsAMTc3l4YNG8rs2bMlIiJC5zVnzpwRANKmTRu9ZbZu3VoAaB1DfR49eiS2trbi6uoqd+7cUdLv3r0rhQsX1ns9Z+b90rJlS7GwsNCpxw8//CAAZMWKFUqak5OT+Pr66nwZJiQk6P0SGjFihACQY8eOpdvGjLwLAfOePXuM+rLMLxPlGettPy9E74rcDJgN+fPPP/XewH/d48ePZfv27VKpUiUpWbJkuje435aAWeP27dvy888/i5OTkzRq1MjghGcampsIPXr00Nn24sULqV27thKcDB8+XHr16iVqtVoqV66s96FGZuRkwDxmzBgxNzeXb7/9Vu7cuSOPHz+WDRs2iKurq5QoUSLDGyMirx70ODo6ir29fboTqf72228C6E5o9bqcDJgbN26sd3uZMmVErVZnufzXvQ0Bs4hIx44dBYC4ubnJ4MGDpX///lKwYEHlukz7YK5fv34CQIoWLapz02HhwoXKjZW8lGOjqDt06ICpU6diypQpmD17NmbPng0TExNUqVIFnTt3xpAhQ2BnZ2d0eaVLl9ZJq1ChAho2bIjQ0NAcqbOtra1Ol+iePXtiyZIlOHHiBOrVq5fu601MTODm5qaTXr9+fVSsWBGhoaGYNGlSjtTVy8tLa9IoExMTBAcHY/v27Vi9erUyeUSBAgX0vn7AgAEYP348QkND4efnp7VN37jitMs3rFixAnFxcZg1a5ZW+aampggKCsKGDRuwfv161KpVK0tta9SoEVq1aqX8v6WlJbp27YpJkybh4sWLqFy5MgDgxx9/RNGiRREcHKzVTbNkyZLo27cvZs+ejbNnzyr5Dbl586beriwAcPbsWbRp00bpMlK4cGH06NEDrVq1QtOmTdMd86wZy2Qojyb96dOnBs8TAPzxxx+IjY3F6NGjUbhwYSW9UKFCGDZsGD7//HOd12Tm/dKnTx/89ddfWLVqldZkeMuWLYO9vb1OV3UrKyudLmmGJmgpVKgQgFfH+L333jPYRg1DY7euXLlisKuQPs2bN9ealOVtcOHCBaPyde/eXWvynfzO0LABIsr/NN9T+sbmatLSTuKTWa1atYKLiwv+/fffdPM5Ojri/fffh6enJ8qVK4fhw4cbHEqUG9I7DmnTs3IsihQpgj59+kCtVuPDDz/EvHnzMGrUKL15P//8c8yfPx/t27dXJiFLy8LCAjt37sTkyZOxfv16zJ8/H0WLFsXnn38Od3d3dO3a1eDvHWMYcxzs7Oz0dltP6++//8bMmTMxatQoBAYGKukdO3aEiKBz5874/vvvMWXKlHTLKV68OJo2bYr169cjIiICVapU0ZtP0x37Ta6zbOg4u7q64urVq0hJScmR3yn29vbZvi4ze31n5TpYs2YNZs+ejZCQECxevBgFChRAQEAA2rdvjwYNGmgdL81+mjRporM0aZs2bTBgwACl23ZeydFpx8aPH4+BAwdi69atOHDgAP7991+cPHkSJ0+exLJly3DkyBGjP1xu3bqFqVOnYseOHbhx44bWgHlj1r0zRvny5XWCRc3g9ydPnhhVRmRkJKZOnYrw8HDcvXtXa9ytu7t7jtQTAGrXrq2T5uvrCzMzM5w+fVorffv27ZgzZw6OHj2KR48eITU1VdmWdtxp69atMXbsWLRv3x7du3dHs2bN4OfnB1dXV63yNOODd+/erfMlp2mvsT/+9fH29tZJe/08xMXF4fz58yhbtiy++uornfyasboXLlzIMGB+9OiRwQkgHj9+rATLFhYWaNasGZo3b47GjRtne4IwY2nOp74JMOrWrav3NZl5v7Rp0wYuLi5YunSpEjCfOXMGx48fR79+/bTeE507d8aiRYvg6+uLzp07o1GjRnjvvfcMfuhr1v57+PBhJlqcffv27Xuj+3uT7t69i7t37+Z1Nd6ohIQENGjQAADwzz//cG1vojdEs77tlStXdG56XrlyRStPVjk7OytlZcTNzQ3lypXL1ERjOcHW1haFChVCVFQUUlNTdQLCnDgWmkmb9u7dqzdgDgoKwvTp0/H+++9j7dq1BmcKtrGxwYwZMzBjxgytdM1vJR8fnyzXMe318LrU1FRER0ejfPnyGZajmbzt9Qc2adNOnjxpVJ00k/gaWoUhJiYG27ZtQ9myZVG/fn2jyswJMTExetPv378PtVqt9bspM/MXlSpVSmscdrly5XDo0CHExMToBOnGXpdlypSBSqUy+D68cuUKnJyclIc7WbkOzMzMMG7cOIwbN04rXTPPTdrrUhMv6YsRNWkJCQnptim35fg83U5OTujRowd69OgB4NWA7169eiEsLAyTJ082aoHzBw8eoEaNGrh37x6aNGmCtm3bwtbWFiYmJti0aRNOnTqVI3XVFwBpPpCMeUoSGRmJGjVqICkpCe+//z7KlSsHGxsbqFQqLFu2DC9evMiRegL671yZmJjA2dlZ647PqlWr8NFHH8HV1RXNmzdHsWLFlKeBwcHBWnUqU6YM9u/fj0mTJmHFihX4+eefoVKp0KhRI8yZM0d5+qeZeGj27NkG65ed5WOMOQ9PnjyBiODy5cvKJG9ZrYe1tbXBpY/q16+PS5cuYevWrdi6dSvWrl2L5cuXw8zMDHXq1EHLli3RsmVLvUF5Tt2R1uTTd85fv5kBZP79Ym5ujp49e+Kbb77BmTNnULlyZeXOde/evbXyzp07F25ubli6dKnyoefi4oLhw4fj888/15mQSfOBZmyAc+7cOb3pmZ0RtV69em/lE2ZjAuHChQu/dU+Ys3sDIzU1FUePHlX+TURvRv369TFjxgzs3r0bnTt31tq2a9cuJU9WPXnyBJcvX1Zmys2IiODu3bt5sqxM/fr1sWHDBhw+fFirB11CQgL279+PKlWqZOtGuuYBhr62zZw5E8HBwfD398fGjRsz/aBIRLB+/XrY29ujWbNmWa6ju7s7XF1dsX//fiQmJmr1Lvv3338RGxtr1PWguYmvb+koTZqxbdR8N+ibgRl49Ts4KSkpV5eS0ufEiRNISEjQ+v1z69YtXL9+HdWqVdPKm97v2Nf5+flpBcz169fHoUOHsHv3bnTr1k0r7+7du1GwYEF4eXmlW6ZarUa1atVw7Ngx3L59W2um7Fu3biEyMhJt2rRR0nLqOgCAtWvXwsTERKs3o2bS54iICJ38mjR9PXrfqDfR7/vq1asCQGrUqKGkpTeGecaMGQJAVq5cqbNNM243rayOYda3ILqh/PoMHDhQAMj+/ft1tnl4eEjJkiW10rIzhlnfOIyUlBQxNzfXGoPk6ekpRYsW1RkLcvfuXYNjdURE4uPjZffu3TJw4EAxNTUVNzc3ZRxBhw4dBIDcvHnTqDpndgyzvmP9ev6nT58KAGnSpIlRdUhP2bJlpUiRIkbljY2NlU2bNkm/fv2kePHiytjMEiVKyKeffqq1UHtOTfqlmWhr586dOtv++ecfnWOW2feLyKsx2gBkxIgRkpSUJK6uruLh4ZFuva5cuSILFy4UT09PASDz58/XyRMUFCQAZNeuXRm2Mz0cw/xuj2F+288t0dtA3+dsYmKiFC9eXNRqtURGRirpERERolarxcfHRyv/y5cvJSIiQmc+F30T9CQkJEjnzp0FgHz55Zda2/TNe5Gamqp8p3Tr1s1gO3JjDLOIyNatWwX/PzlRcnKykh4cHCwAZM6cOVr5Y2JiJCIiQp48eaKkPXjwQO9cN/Hx8cr388KFC7W2zZ07VwBI7dq1050LSOP1eWtSU1NlwoQJAkCmTp2qk18zUa6xv0VHjx4tAGTy5MlKWlJSkjRr1kxUKpXOub527ZpERERojTH+9ddfBXg12W7aNqWkpEiPHj0EgHz//fdK+rlz57QmMdOYPXu2AJB69eoZrK+Pj4+oVCqj2peTY5gB3cnI+vfvr/dayY4zZ86IiYmJVK5cWWsumZCQEAF0J8x98uSJRERE6MQF8+fP1xtf9O7dW6BnfqPMXgf65lP6+eefBYD069dPZ5u/v7+oVCqteQFevnwprVq1EgDy448/Gjgib8YbuWVna2sLQPvpn6Z7i76nB5pF3DXjcjUSExNx/PhxnfzplZWbrl69ioIFC+p0nb137x6uXLmis7ZZdup58OBBnbSjR48iKSlJ62nn1atX0bp1a511p/fv359u+dbW1spSNi9fvsSSJUtw+vRp1KxZE9WrV8fGjRtx6NChLC3FlBPs7e3h7u6O06dP69zdyqyKFSvizz//xPPnzzMcV29jY4N27dqhXbt2AIBTp07hr7/+wtatW/Hzzz+jePHiSpfyzN6xM0QzJufAgQPKupca+s5jZt8vAODp6YnatWvj119/RZ06dXD//n29i8WnVaZMGXz66ado1aoVSpQogS1btmDQoEFaeS5evAgAWstekH7+/v7w9/fXWmpNXx59XdiIiHKDpaUlfvzxR7Rr1w61a9fGhx9+CODVU7vU1FSd5XFu3boFT09P+Pn5aX2W9erVC/Hx8ahevTqKFy+Ohw8fYteuXbh27Rpq1qyp002zcePGyvdp8eLF8fjxY+zfvx/nzp2Dm5sbZs6cqZU/MjJSWdZR01Nn69atyr/r1auHvn37ZutYtGzZEl26dMG6detQs2ZNNGnSBBEREdi8eTNq1aqFAQMGaOWfN28egoODsXTpUuWJ4I0bN+Dr64s6derA3d0dLi4uuHPnDrZv34579+6hadOm6NOnj1LGnj17MHToUJiYmKBmzZp6e/b16tVL6wl9zZo1UapUKXh6esLExAS7d+/GyZMn0bFjR4wZM0bn9fL/SzIZ+9R+/Pjx+OOPPzBx4kQcO3YMHh4e2LVrF44dO4YRI0agatWqWvl79uyJ8PBwREVFKfXs0qUL5s6di0OHDsHDwwNt2rSBlZUV9uzZg9OnT6Ny5cro16+fUsaiRYuwcuVKNGjQAG5ubkhJScHBgwdx9OhRuLi4YNGiRXrreubMGZw4cQL+/v5G92LIKfXq1cO0adNw8OBBVKpUCQcOHEB4eDiqVaumc61kR6VKlTBq1CjMnDkTPj4+aNeuHW7duoW1a9eiXLlymDBhglb+jRs34pNPPsGkSZO0uoL369cPq1evxpIlS3D16lXUqFEDhw8fRmhoKD744AOd35SZvQ46deqEly9fokqVKrC2tsbBgwexd+9e1K1bF99++61Ou3788UfUqVMH77//Pjp27IgiRYpg9+7dOH36NBo2bJjt93O25VTk/dNPPxmc8nvUqFECaE9tv2XLFp07FRpTpkwRALJ9+3YlLTU1VYYPH67cxUlLczdu+fLlOmXl5hPmvn37ikqlkvPnzytpL168UGaOfv0J88iRIwWA/PPPPxmWrZHeLNmaJYbS3gUqV66clC5dWmuWudu3bytT8ae9k3rs2DG9dy81SzdpzuetW7fE2tpaypYtq/dOaXR0tNadvNx4wizyamkuADJo0CCdpRhSU1ONXlZK80Q27YzhWfHw4UOd2akze8dOn0ePHomNjY3OLNn37t3TO0t2Zt8vGpo7fUWKFBFTU1O5ffu21vbExET5999/dV537NgxAV4tCfe6cuXKibu7e4ZtzMi78IRZ5NW5NrS0lL+/vzx69Civq5hpfMJM9HZI73N2z549yjKBdnZ20rx5c71PgTW/mV7/PTVnzhxp1KiRFClSRCwsLMTW1lZ8fX1l5syZWqtfaHz33XfStGlTKVq0qJibm4uNjY14e3vLl19+qfdzMO1vI31/hp4avy6jvC9fvpSpU6dKuXLlxMLCQooXLy6BgYF6fztpfvuk7Tn56NEjGTdunNSoUUOcnZ3FzMxMnJycpEGDBrJw4UKtJ9ci//vtk97f672OJk6cKBUrVhQbGxuxsbGRGjVqyKJFiwyuGuLs7KzV49MYMTEx0q9fPylUqJBYWlqKl5eXzJs3T+8+DD3Bjo2NlYkTJ4qXl5dYWlqKhYWFuLu7y5gxY3SeRm7btk06dOggpUqVErVaLZaWluLu7i7Dhg3T+a2SluZ39rJly4xqV04+YQ4ICJCjR4+Kv7+/2NjYSIECBaRv37658j2empoqCxculEqVKomlpaW4urpK79695d69ezp5NdeUvjbGxsbK6NGjpUSJEmJhYSFlypSRyZMnG5yBPDPXwbx58+S9994TOzs7sba2lsqVK8vMmTP19hzQuHz5snTr1k2cnZ3FwsJCypcvL0FBQXo/M960HAuYNesqe3p6Sp8+fWT8+PEycOBA8fLyUrqw3rhxQ8kfExMjlpaW4uTkJIGBgfLVV18pAW90dLTY2NiIWq2WTz75RIYPHy7VqlUTJycn5Y2Y1unTpwWAFC9eXMaOHStfffWVbN68WURyN2A+dOiQmJiYiJOTkwwYMEA+++wz8fDwkNKlS4u3t7dOwKy5SeDp6Snjx4+Xr776KsMgT986zOPGjZP33ntPAN0ljDTdVSpUqCAjRoyQXr16ibOzs9KtOu0Xw7Bhw8TGxkZatmwpQ4YMkdGjRytrNjds2FCr3DVr1oi5ubmo1Wrp3LmzjBkzRvr27Sv16tUTExMTWb16tZI3twLmlJQUad++vQCQ8uXLS9++fWX06NHSrVs3KV26tFhaWqZ7LDUiIiIEgEycOFFn28WLF2Xw4MFG/6UNUkVefbnWq1dPOYZjx46Vhg0bCgD54IMPjKqfyP+m0S9UqFCG6zBn9v2i8fz5c7GxsREA0qpVK53tjx8/FgBSsWJF6dGjh4wbN05Z99rMzEyn2/W1a9cEeLW2ZHa9KwGzyKsvvtDQUOnTp4+0adNG+vTpI6GhoW/d+ssaDJiJ3g7GfM7+12UmuP4viIyMFACyZs2avK5KvpDTATP9d+VYwBwZGSnTpk2Thg0bipubm1hYWIiNjY1UqVJFxo0bp7Wur8bvv/8u3t7eYmlpqRPAHjp0SPz8/MTOzk6cnJykXbt2EhkZqYwDft3ChQulQoUKypq5mgs3NwNmkVd3wapXry5qtVpcXFykR48ecvv2bfHz89MJmEVEJk+eLCVLlhRTU1Oj9pP2zbxjxw6pUaOGWFlZSeHChWX06NE66/elpKTIt99+K+7u7mJpaSmlSpWSL774Ql68eKHzhj548KD07dtXPD09xdbWVuzt7aVKlSoyY8YMvWvsHjt2TLp27SqFCxcWc3NzKVy4sNStW1dmzpypNTYitwJmTfsWL14stWvXFjs7O7GyspIyZcpI586dZcOGDekey7Tq1q0rFSpU0EnP6K71639fffWVThmZvWNnyK+//iqVKlUSCwsLcXNzk0mTJsmlS5f0HrPMvl80NGvt/fbbbzrbXr58KdOmTdN6SlC8eHHp0KGDHD58WCe/5sl92h4XWcUfcu82BsxEuY+fs6L1fV6xYsW8rk6uW7Jkibi5uek82X7XaHosav4YMFNGVCL/P5iB6B2yfv16dOnSBQcOHNC7ZNe7wtvbG7dv38atW7eytVybiKBy5cooXLiwMpNqdmhmyTY0izb9t8XFxSnjz6Kjo2FjY5O3FSL6D+LnrPbyPq6urjrzctB/07///ovt27cr/6+ZUyQroqOjUbp0aQQEBGDZsmU5U0HKd978PP1E+UCnTp1QrVo1TJ06FX/++WdeVydPhIaG4vTp0xgxYkS21zbftGkTzp07pyxPRZQdNjY2Bte0JCLKKZlZD5f+O2rVqqW1VBhRRhgw0ztJpVLhp59+wp9//qmzbt5/3apVqxAREYElS5ZArVZj+PDh2S4zKSkJCxcuRPXq1bNfQSIiIqK3QKlSpcDOuv99DJjpnVWtWjWdxeTfBYsWLcLevXvh7u6ORYsWoUSJEtkus0uXLjlQMyIiIiKi/IUBM9E7Jr11f4nyg4SEBLRo0QIAsG3btneqBwgRERHlLwyYiYgoX0lNTUV4eLjybyIiIqK8YpLXFSAiIiIiIiLKjxgwExEREREREenBgJmIiIiIiIhIDwbMRERERERERHowYCYiIiIiIiLSg7NkExFRvqNWq/O6CkREREQMmImIKH+xsbFBXFxcXleDiIiIiF2yiYiIiIiIiPRhwExERERERESkBwNmIiLKVxITE9GqVSu0atUKiYmJeV0dIiIieodxDDMREeUrKSkp+Ouvv5R/ExEREeUVPmEmIiIiIiIi0oMBMxEREREREZEeDJiJiIiIiIiI9GDATERERERERKQHA2YiIiIiIiIiPThLNhHlO9evX0dSUhIqVqyY11WhPJCamqr829fXFyYmvLdLlNOuXLkCc3PzvK4GEVG+x18hRJTvJCQkICkpKU/2nZqaipiYGK2gLSdduXIFV65cyZGycqKu6ZWRXl2zsm9jX2NiYgJLS0tYWlrmWrCck+chJ8t6m/adFXlZ33ftvZ1RfczNzWFjY5Pl8omI3hUqEZG8rgQRUVqaJ8vnzp174/uOjo5G6dKlERUVhVKlSuV4+TnZtpyoa3plpFfXrOw7M6/J7WsgJ8vPy+s1L/edFXxvGycn6vq2XRtERPkVnzATERERERER6cGAmYiIiIiIiEgPBsxERGk4Ojpi0qRJcHR0zOuqZCgn6prVMrLyurfp2NJ/z9t0/b1NdSUi+q/jGGYiynf+y2Pv3qa2/ZfH5nIMc9542+qbGfmtbfmtPkREbys+YSYiIiIiIiLSg0+YiYiIiIiIiPTgE2YiIiIiIiIiPRgwExEREREREenBgJmIiIiIiIhIDwbMRERERERERHowYCYiIiIiIiLSgwEzERERERERkR4MmImIiIiIiIj0YMBMREREREREpAcDZiLKUWFhYWjYsCHs7Ozg6OiIFi1a4MSJE0a99sSJEwgICEDFihXh5OQEtVoNT09PDB06FLdu3dL7muTkZEybNg3u7u6wsrKCm5sbRo4cidjY2JxsFoDstS0sLAwqlcrgX2Jios5rstu27NTX398/3fqamppq5V+2bJnBvIUKFTJqn6/76aefUKVKFVhbW6Nw4cLo27cvpk6dio8//hgeHh4wMTFRjl1mjtWJEycwfvx4VK9eHTY2NjA1NYWlpaXBc5Fe2woXLpylcxsdHY1WrVpBrVYr+ezs7PDxxx8jKioq28fp/v37evNm9Zp69OgRhgwZgho1asDV1RWWlpYoXbo0OnXqhGPHjuW7+mZGeudu+/btRpeTE+0aP358uvVRqVTYu3fvG6tPbnyOEhG9bVQiInldCSL6b9i6dSvatm0LBwcHfPjhhwCAVatWITExEeHh4ahevXq6r1+4cCEmT56M2rVro0SJEjA3N8eZM2ewY8cOODk54d9//0X58uW1XtOtWzesXbsW1apVQ+PGjREZGYnNmzejdu3aCAsLg4WFRb5omyZ49fPzg7+/v872L7/8EmZmZjnWtuzWd9myZYiOjtZJj4yMxNq1a9GsWTP8/fffWvk/+eQT5f8dHBzw4sULJCYm4uuvv8bnn3+e7v5eN27cOMyYMQPu7u5o164dbt68iXXr1iElJQUAULJkSTx9+hRPnjxBQkICevXqZfSxqlWrFo4cOYI6depg3759AABzc3MkJSUBAGJiYuDs7KzTtnbt2qFq1apa9bS1tYWvr2+mz62npyciIyNhZWWFChUqIC4uDpcvXwYAODo6Yu/evahUqVKWj1Pp0qVx+PBhODk5aeXP6jUVGRmJ6tWro06dOihbtiwcHBxw7do1/PHHH0hMTMSaNWvQuXPnfFPfzFCpVChZsiR69eqls61Hjx4oV67cG2uXl5cXPvjgA50bUomJiZgxYwYcHBxw9+5dWFlZvZH65PTnKBHRW0mIiHLAixcvpHjx4qJWqyUyMlJJj4iIELVaLe+9916GZSQmJupNX7ZsmQCQ3r17a6Vv3bpVAEizZs0kOTlZSQ8ODhYAMmfOnCy2RltOtC00NFQAyKRJk4zaZ3balhP1NWTAgAECQFauXKmVvnTpUgEggYGBEhMTIyIifn5+AkASEhIytY8zZ86IiYmJVK5cWeLi4pT0kJAQASADBgzQKn/jxo2ZOlbz5s2TqKgoERH566+/JCYmRpKSksTZ2VkAyOTJk/W2benSpXrrm9VzW6NGDb31BSDvv/9+huVkdJwCAwP17jcr11RSUpIkJSXppEdERIiVlZWUKVMmX9U3MwCIn59fll//Jtq1Zs0aASD9+vXLF/UhInqXMGAmohzx559/CgD59NNPdbb169dPAMjx48ezVPbTp08FgPj7+2uld+rUSQDIwYMHtdLj4+PF3t5eqlSpkqX9vS4n2pbZoCo7bcutc5GYmChOTk5ib28v8fHxWtv0BZVZDZhHjRolAGT16tU629zd3aVgwYKSlJSklN+hQ4ccuQ58fHwEgDRv3jzDtqWVk+fWzs5OVCqV2NjYZFiOscfJmP1m5/3i4+MjJiYmkpqa+lbU93XZDZjfRLtatGghAGT//v35oj5ERO8SjmEmohyhGVfXuHFjnW1NmjTRypNZmq6/FStW1Nmnra0tatSooZVubW2NunXr4vTp03j27FmW9vn6foCcadvFixfx3XffYcaMGdiwYYPB+mWnbbl1LjZv3ozHjx+jS5cusLa21pvn+PHjmDVrFmbNmoWHDx9meh9p66av/o0bN8bDhw9x/vx5JW3//v05ch2oVCoA0Ok+rZG2bVu2bNEZm5xT51ZEDNbh9XIA449TbrxfoqOjcfHiRXh6eirHLz/X15DHjx9j4cKF+Prrr7F06VLcuHHD6Nfmdrvu3r2LHTt2oHz58qhTp06e14eI6F2T8TcyEZERNOMvy5Ytq7NNk6bJk5EDBw5gx44dSExMREREBP766y+UL18e48ePV/LExsbi3r17qFy5MkxMdO/9pd3ne++9l+n2pJWTbVu9ejVWr16t/L+joyMWLlyIrl27KmnZbVtO1jetkJAQANA71lNj7ty5Omnh4eF4//33jd7P5cuXYWdnBxcXF51t+up///79HLkO7t27BwB6xyEDum0rUqQIVq5cqQSKOXFuNWUZc80ae5yqVKmSY++X27dvY9GiRUhJScGNGzewadMmqFQqvec9P9TXWKdPn8bAgQOV/zczM0NgYCBmzJiR4Y2A3G7Xr7/+ipSUFAQEBBjVlvx8nImI3kZ8wkxEOULzBMLe3l5nmybt6dOnRpV14MABBAcHY8aMGdi8eTOqVKmCv//+G0WLFjVqf1nZZ3pyom0uLi6YPXs2IiMjER8fj+vXr2PBggUAgI8++kiZfCqj/Rmzz5w8Fxr37t3D33//jXLlyqFu3bo628uUKYOFCxfiypUrSEhIwJUrV1CqVCkAwAcffJCpAP3Zs2eZbnt2r4Pw8HBlJvbevXtrbdPXtilTpuDx48do06YNEhIScuTc3rt3T8nbo0ePdOurKcvYdufU++X27dsIDg7GlClTEBISAgsLC/z+++9o2LBhvqyvMUaPHo0jR47gyZMnePDgAbZs2YKyZcti1qxZmD59eoavz+12hYSEwMTEBB9//LFR7cmvx5mI6G3FgJmI8p1Ro0ZBRPD8+XPs3bsXarUavr6++Pfff/O6allWsWJFjBw5EhUqVIC1tTVKlCiBgQMHKk+Pvv7667yuYrpWrlyJ5ORkg0+5GjRogE8//RRlypSBlZUVypQpg5IlSwIA4uPj8e23377J6mZKZGQkOnXqpMxM/Hp3c31t++KLLzB79mzExcVhy5Yt2T63cXFxaNeuHZ4/fw4AKF26dM43NAf4+vpCRPDixQucP38ebdu2RYsWLbBw4cK8rlqWzZw5E76+vnBwcEDBggXRunVr7Nq1C46Ojpg+fboye3peOH78OM6ePYuGDRvCzc0tz+pBRPQuY8BMRDlC8yRC31g3TZqDg0OmyrS1tUW9evWwdetWmJqaom/fvkbtLzv71Cc32qbRqlUruLi4aN0MyG7bcqO+ISEhUKlU6NmzZ6Zep5GZmx329vaZbntWj9XVq1fRuHFjxMfHo3LlykbXEXjVNV2lUhlsm7HnNiEhAW3atMGhQ4eUMarGnJ/MHKecfr9YWFjA09MTP//8M5o0aYLhw4cbXCs9P9Q3s4oXL46mTZvi2bNniIiISDdvbrZLMwzC2O7YuV0fIqJ3EQNmIsoRmrVKr1y5orNNk2bMeqb62Nvbw9fXF+fOncPjx48BvAqmCxUqhKioKKSmpub4PtPKzbYBgLOzM+Li4pT/z27bcrq+J0+exOnTp7P8lMva2lqrfRkpV64cnj9/jpiYGJ1t+urv6uqapWN17do1NGzYEA8fPsSmTZsyHRSo1eoM25bRuX3x4gU6dOiA0NBQBAcHK3Uw5vxk5jjl5vulUaNGePHiBQ4fPvxW1NdYmrW4M7p2c6tdSUlJWLVqFezs7NCxY0ej6/22HWciovyOATMR5Yj69esDAHbv3q2zbdeuXVp5suLOnTsAoHSb1ZQXGxur80M9ISEB+/fvR5UqVQyOzcuM3GzbkydPcPnyZaX7ctp9ZrVtOV3frDzlSishIUGnfelJr/67d+9GwYIF4eXlpaTVrVs308fq9u3baNy4MW7fvo1169ahadOmmWkSACAiIgLx8fEG25bRuT1w4AC6dOmCv//+G2PGjMHo0aMzdd1m9jjl1vtF897MaGbv/FJfYx09ehQAMrx2c6tdf/31Fx48eIBOnTpBrVYbXe+37TgTEeV7ebysFRH9RyQmJkrx4sVFrVZLZGSkkh4RESFqtVp8fHyUtJcvX0pERIRcu3ZNqwxDawNr1sGtVauWVvrWrVsFgDRr1kySk5OV9ODgYAEgc+bMyYmm5UjbTpw4oVNuQkKCdO7cWQDIl19+mWNty4n6aiQlJYmrq6vY2tpKbGyswX3qa1/dunUFgACQn3/+WWe7oX2fOXNGTExMpHLlyhIXF6ekh4SECAAJDAwUkf+t87xx40a9x2rixIl610e+f/++eHp6iqmpqaxdu1ZJN7RutKZtaev7+PFjqVevngCQiRMn6rTNmHNbqFAhASBDhgwREcPnNrvH6fX9ZuWaOnv2rLx48UIn/dSpU2Jvby9qtVoePXqUb+prrHPnzult1+zZswWA1KtXT0l70+3SrC8eHh6ut+5v03EmInqbMWAmohyzZcsWMTExEScnJxk8eLAMHjxYnJycxMrKSg4dOqTki4qKEgDi5+en9Xpvb28pW7asdO3aVUaPHi2DBg2SGjVqCABxcnLSG5R16dJFAEi1atVk7Nix0rZtWyW41vdDOC/bVr58efnwww9lzJgx0qdPHylZsqQAkJo1a+oNRrPTtuzWV2Pz5s0CQHr16pXu/hwdHcXb21t8fHykUqVKUrZsWVGpVAJAihcvLj179pSAgACJiYkxat9jxowRAOLu7i6jR4+WDz/8UExNTaVgwYLSvXt3CQgIUALOHj16SKlSpQSAeHt7ax0rANKgQQOtshs0aKAc18aNG4u3t7d4e3uLjY2NAJDatWtLQECAUl9N2zQBTKFChcTFxUUASLt27bJ0br28vASAmJmZSZ06dcTd3V0ASLFixeTLL7/UCvKzcpzKlSunBLBpZfWaGjZsmBQsWFDatWsnw4YNkxEjRkirVq3E1NRUTExM5JdffslX9TXWsGHDxNnZWT744AMZPny4DBkyRHx9fQWAuLi4yPnz5/OkXQ8ePBALCwspU6aMpKam6q3723SciYjeZgyYiShH7dmzR/z8/MTGxkbs7OykefPmcuzYMa08hn7ohYSESJs2baREiRJiaWkpVlZW4unpKcOGDZObN2/q3d/Lly9l6tSpUq5cObGwsJDixYtLYGCgPH/+PF+1bc6cOdKoUSMpUqSIWFhYiK2trfj6+srMmTMlMTExV9qWnfpqdOzYUQBIWFhYuvuaMGGC1K1bV8zNzZVAVd9fVFSUUftOTU2VhQsXSqVKlcTS0lJcXV2ld+/eUqdOnXTLL1mypHKsevfurbd8TTBrzF9UVJTSNmdnZwEgpqamUrduXVm0aJGkpKRk6dx+/PHHGe47O8fp3r17eveb1Wtq79690rNnTylfvrzY2tqKhYWFuLm5Sffu3bVuwOSX+hpr27Zt0qFDBylVqpSo1WqxtLQUd3d3GTZsmNy+fTvP2jV37lwBIEFBQQbr/jYdZyKit5lKRAREREREREREpIWTfhERERERERHpwYCZiIiIiIiISA8GzERERERERER6MGAmIiIiIiIi0oMBMxEREREREZEeDJiJiIiIiIiI9GDATERERERERKQHA2YiIiIiIiIiPRgwExEREREREenBgJmIiIiIiIhIDwbMRERERERERHowYCaiDPn7+0OlUmHZsmU5XnapUqWgUqkQHR2tld6rV69c22dWREdHQ6VSoVSpUjrbNG0ICwt74/VKj+a85bd6UdapVCqoVKq8rkaOCAsLg0qlgr+//xvdb2pqKry9vVGwYEE8f/48y+XkVf3JsKNHj0KlUuGTTz7J66oQ0X8IA2ait9Ddu3fxxRdf4L333oO9vT0sLCxQpEgRVK1aFX379sWKFSsQGxub19XMF8LCwhAUFIRNmzbldVVy1KZNmxAUFPSfCIaDgoKMDjw0AeN/od1vyrJly5TjlvbPzs4OlSpVwrBhw3RuWGXXkydPEBQUhKCgoBwtNycsWbIEp0+fxqhRo2BnZ6e1TXOjLr0/R0fHvKl4Lrl58yYmTZqEevXqoXDhwrCwsICdnR3c3d3RtWtXLF++PFs3Ft4kX19ftGnTBsuXL8epU6fyujpE9B9hltcVIKLM2bdvH9q0aYMnT55ApVKhWLFi8PDwQFxcHCIiInDq1CksWbIEe/fuRb169fK6ullWpEgRVKhQAQ4ODtkqJywsDMHBwQgICED79u2zXI65uTkqVKiAYsWKZas+OWXTpk0ICQkBAIOBppubGypUqAC1Wv0Ga0b5laWlJXx9fQEAIoJbt24hIiIC586dwy+//IJt27bl2GfGkydPEBwcDAAGg2a1Wo0KFSrAzc0tR/ZpjISEBEycOBGOjo4YMmSIwXyurq4oX7683m2aIDsv6p+TRARfffUVpk6dipcvXwIASpQoAW9vb7x48QI3btzAunXrsG7dOgwfPhzLli1D27Zt87jWGZs4cSK2bNmC0aNHY8eOHXldHSL6D2DATPQWiY2NRadOnfDkyRM0bdoU8+bNg7u7u7L9xYsXCA0NxbJly2Bubp6HNc2+adOmYdq0aXldDUWxYsUQGRmZ19XIlOXLl+d1FSgfKVy4MPbt26eVdubMGXTq1AkXL15EQEAALl68CFNT0zdSnxo1arzx99SaNWtw9+5d9O/fH7a2tgbztWjRIsPhIHlR/5z06aefYvHixTAzM8OYMWPw2WefoUSJEsp2EcGxY8fw008/Yfny5Th+/PhbETD7+vqicuXK2LlzJyIiIuDp6ZnXVSKitxy7ZBO9RbZu3Yp79+7B1tYWGzdu1AqWgVdPkJo3b441a9agZs2aeVRLInpbVK5cGXPnzgUAXL16FceOHcvjGuWuhQsXAgB69OiRxzXJW7/++isWL14MExMT/P7775gxY4ZWsAy8Gv7g6+uLxYsX49ixY6hYsWIe1TbzNOf3p59+yuOaENF/AQNmorfI1atXAQAVKlSAjY1Npl6rGSea3phCYyYUioqKQo8ePVCkSBFYWVnBw8MDU6dOxYsXLwy+5ujRo2jTpg2cnJxga2uLatWq4eeff053P+lN+rV69Wo0btwYBQsWhLm5OZydnVGxYkX0798fhw4d0mqPpltoSEiI1jjEtN2YNfsKCgrCw4cPMXToUJQuXRoWFhZKN+70Jv1K68SJE+jQoQNcXFxgbW2NqlWrYsGCBUhNTdXJqxlb2qtXL4PlvT4pmqYemu7YwcHBWu1KW1ZGk35t2rQJzZs3h7OzMywtLVGiRAn06NEDp0+f1ptfcw316tULSUlJmDZtGjw9PWFlZQVXV1d8/PHHuHHjRrrHJ7ckJCRgzZo1+Oijj+Dl5QV7e3uo1Wp4eHggMDAQd+7c0XmNZtKmjP7Svmeysh/gf+fa398fycnJmD17Nry9vWFjY6MzJnbHjh1o2LAh7O3t4eDggHr16uH333/PycOlJe3NtaioKJ3tu3btwtChQ+Hj4wMXFxflWvnwww9x/Phxnfy9evVC6dKllf9//XhqruWMJs26d+8eRo4cCQ8PD1hbW8Pe3h41a9bEnDlzlC7EmXH16lUcPnwYTk5OOdL1PL36p23rsWPH0K5dOzg7O8Pa2ho+Pj5YunRpumVfu3YNQ4YMUYZUaNq+YMECJCcnZ6veqampyjXdv39/tGnTJsPXVKpUCZ07d9ZK03wWab4zNm7cCH9/fxQoUAAqlQonT54E8GrOjfnz56Nly5YoW7asci5r1KiBb775Rue7IykpCYULF4ZKpcLevXsN1mnjxo1QqVSoUKGCzrZ27doBePVdQUSUXeySTfQWsbe3BwBcunQJjx49QoECBd7o/i9fvowRI0YgNjYWlSpVgp2dHS5cuIAvv/wSu3btwrZt22BlZaX1mi1btqBjx45ISkqCnZ0dPD09cfv2bfTr1w9nz57NdB0+//xzTJ8+HQBQqFAheHt7IzY2FtHR0Th//jzUarUSANStWxfXr1/HjRs3dMYkVq5cWafsmJgYVKtWDTdu3ICXlxe8vLxgZmb8x+SBAwfw1VdfwcTEBJ6ennjw4AFOnTqFwYMHY9++fVi5cmW2Zzi2srJC3bp1cenSJdy/fx8lSpTQGkP5eq8DQwYMGKA8fSlatChKly6NS5cuYeXKlVi3bh1CQkLQvXt3va9NSkpC8+bNsWfPHri7u6N8+fK4cOECfv31V4SHh+PkyZNv/No8duwYunfvDlNTUxQqVAjly5dHQkICoqOj8f3332PNmjXYt28fypYtq7zGwcEBdevWNVjm4cOHkZSUlO39pCUiaN++PbZu3YrSpUvDy8sL9+7dU7b/+OOPGDRoEACgYMGCKF26NC5cuICOHTvi22+/zc4hMig+Pl75t77x7s2bN0dKSgqcnZ1RpEgRFC9eHNeuXcPq1auxYcMGrFu3Tmt+AHd3d/j6+uLo0aMAoHOMX/+M0OfUqVNo1qwZ7t+/D3Nzc1SqVAnx8fE4fPgwDh8+jHXr1mHbtm3KZ6IxwsPDAQDVq1d/YzONb9u2DcOHD4e1tTXKlSuHGzdu4OTJk+jduzcePnyIUaNG6bzmzz//RLdu3RAXF6e87vnz5zhy5AgOHz6MP/74A1u2bIGFhUWW6nTkyBFcuXIFADB48OBstU9j5syZGDt2LFxcXJR2avz888+YMGECrKysUKRIEVSuXBkPHz7E8ePHceTIEWzcuBF79uxR2mNubo6AgADMnDkTv/zyC+rXr693n7/88gsAoHfv3jrb3N3d4ejoiPv377NbNhFlnxDRW+PSpUtiYmIiAKRq1aqyatUqiYmJMeq1kyZNEgAyadIkg3kAiL6PBT8/PwEg5ubmUq9ePbl9+7aybf/+/VKwYEEBIOPHj9d63d27d8XR0VEASJ8+fSQ+Pl5ERFJTU+WXX34RU1NTMTMzEwASFRWl9dqAgAABIEuXLlXSYmJilNds2LBBUlNTlW0pKSmye/du2bx5s952BwQEGGy3Zl+mpqZSo0YNiY6OVrZp6hwVFSUApGTJkjqvL1mypHJ8PvjgA3ny5ImybePGjWJlZSUAZNGiRVqvW7p0aYZ105Rt6Pikdz415y00NFQrffHixQJALCwsZOXKlUp6YmKiDBkyRACIlZWVnD9/Xut1mmNpbm4u5cuXl9OnTyvbrl27Jp6ennqvg4xoyvXz88swr+Yafb1N0dHRsmbNGnn69KlW+vPnz5XymzVrZnSdfvjhBwEgTk5OcuHChWzvR3OuTU1NxdnZWf755x9lm+YaO3PmjJibmwsAmTBhgiQlJYmISHJysnz11VfKtsx+dWv2re/aFRFZtGiRABATExO5du2azvYff/xRrl+/rpWWkpIi69evFxsbG3FycpLY2Fit7Zr3S3p1DQ0N1XveExISpGzZsgJAGjVqJHfv3lW2HTlyRIoWLSoApGfPnhm0XFufPn0yvD4176v03pMZ1V/kf9epubm5TJgwQV68eKFsmzVrlgAQtVqtcx2dP39e1Gq1mJqaysyZM7Ved/LkSfHw8BAA8sUXX2TcYANmz56tXNvZkfYcW1hYyPz58yUlJUVEXl0fmrrv3btXdu7cKS9fvtR6/Y0bN6R9+/YCQL7++mutbRcvXhQAYmNjI8+fP9fZ9507d8TMzEzMzMzkzp07euvXtGlTASALFy7MVjuJiBgwE71lZs2aJSqVSvmhAkBKlSolnTp1knnz5sn9+/f1vi4nAmYLCwu5deuWznbND3I7OzutHzdBQUECQMqUKSPJyck6rxswYICyT2MC5oMHDyo3C4yVmYDZ0tJSbty4oTePMQGzi4uLEvzoq0OZMmW0gvy8CJhTU1OldOnSAkA+//xzva/z9fUVAPLJJ5/obQcAOXjwoM7rfv/9dwEg3t7eBuukT9pyjf17PWDOSN26dQWAwR/Xae3YsUO5MbNr164c2Y/mXAOQ9evX631tr1690r1x0Lx58xwLmFNTU+XGjRuyYMECsbe3NzpIfN0XX3whAGTNmjVa6dkJmJctWyYAxNbWVh4+fKjzui1btigB/uvvi/Q0btxYAMi8efMM5tG8r4y59owJmJs3b653P97e3gJANm7cqJXepUsXASATJ07U+7oTJ06ISqUSe3t7SUhIMKrdrwsMDBQA4uPjk6XXa6Q9x4MHD85SGXFxcWJubi4eHh462/z9/QWA/Pzzzzrbpk+fLgCkbdu2BsvWnEtDn3NERMZil2yit8yoUaNQp04dfPPNN9i2bZvSHTQ6OhobNmzA6NGjMXHiRIwbNy7H9/3BBx+gaNGiOukffvghRowYgcePH2P//v14//33AbzqjggAgwYN0jvz7tChQ5VJeIyh6Xp88eJFnDhxAj4+PllphkFNmjRB8eLFs/z6Pn36wNraWid98ODBCA4OxtWrV3Hx4kW9Y+7elAsXLijjVIcPH643z4gRI/Dhhx9i+/bterd7e3ujVq1aOumaNE13z8yyt7fX21U+rf379xvcJiLYuXMn/vrrL1y+fBnPnj1Txo5funQJAHDy5Ek0b97cYBkXLlxAly5dkJKSggULFqBx48Y5uh97e3t06NBB77417xdDyx0NHTrU4DkxxrVr1/R2RVar1Rg+fDhmzpxp8LVnzpzB+vXrcfbsWTx+/Fjpqn7//n0Ar8bud+3aNct1S0tzHD7++GO9Xftbt26NChUq4MKFC9ixYwf69+9vVLkxMTEAYNRwgfSWlcrMUneffvqp3vSaNWvi1KlTWu+Vly9fYsuWLQBgsE1Vq1ZFqVKlEBUVhePHj6NOnTpG10VDs6ZyevNg2NraIi4uTiutYMGCePDggd786c3DALwa+79hwwb8888/uHHjBuLi4iAiAAATExNcuHABCQkJWp+fffv2RVhYGJYsWYI+ffpolacZA/56elqa86y5RomIsooBM9FbqE6dOqhTpw6SkpJw4sQJHDt2DDt27MD27duRkJCAzz//HCYmJhgzZkyO7tfLy0tvuoWFBcqVK4cjR47gwoULSsCsWXLF0Ovc3d1hZmZm9CQ2RYsWxYcffohVq1ahWrVqqF27Nho2bIi6deuiQYMGmZ4I7XXZHedmqJ0uLi5wcXFBTEwMLly4kOcBs6ZOrq6uevNogtY7d+7g2bNnOuNEDQUShQoVAvBq+bOs8PHxMThBmYahsafPnz9H+/btsWfPnnRf//DhQ4PbHj9+rKxx/tlnn2HgwIE5vh93d3e9N4+ePHmijGU2dB0ZSjdW2nWYX7x4gaioKDx8+BBqtRp+fn4Gl6IbPXo0vvnmGyXA0Se945pZmmu0UqVKBvNUqlQJFy5cyNSyTomJiQCMG0NtzLJSxsjMe+XSpUtISEiAiYlJujcfNEHrzZs3Aby6WZHemtIamiXFNOtIvx4Qp1WnTh1lbPvTp08znG8ivc/Oc+fOoXXr1spkb4Y8evRIa537jh07YsiQITh48CAiIyPh4eEB4NVNswsXLqBw4cJo2bKlwfI0wbfmvBMRZRVnySZ6i5mbm6NGjRoYOHAgNm7ciIiICGXpjylTpmRpJtn0GAqwgP/9ANQ8vQD+92PQ0OtMTU1RsGDBTNVh6dKlmDp1KkqVKoUDBw5g6tSpaNmyJVxcXDBgwAA8ffo0U+Wlld2AO7PHJy9o9q+pjz6FCxfWyZ+WoeNkYpJ3XykjR47Enj17UKZMGaxevRrXrl1DYmIi5NXQI3z88ccAoDOJl0ZycjI6d+6MS5cuoVmzZvj+++9zZT+Gjl3awMnQdZTeOTOGZh3mffv24ciRI7h79y7mzJmDhw8fonPnzlozzGusWrUKs2fPhqWlJb777jucP38esbGxSE1NhYhgyZIlAAy3Nysyc41m5v2k+ax5/PhxNmqXORm9V9LehHjy5AmAV7NY79+/3+Cfps1pA9r08mv+NDRBaXoB7I4dO5RrRbPsWFbamZKSgk6dOiE6OhqNGzfGzp07ce/ePbx8+VJ5z2iWs3r9GrKyslLeT5oJvtL+u2fPnulOyvjo0SMAyPR3DBHR6xgwE/2HlCpVCjNmzADw6ofk+fPnlW2aJ3OGnhKl97RBI72ubZqnY5qnF8Crbn3pvS4lJSXTT6YsLCwwfvx4XL16FVevXsXy5cu11tz88MMPM1VeTsrs8cnonADGnZfM0Ow/7czMr7t7965O/vwsOTlZWT5m8+bN6NatG9zc3GBpaankyeg6Gzp0KHbv3g0PDw+sW7dO71PgnNiPIZr3CmD4OkrvnGWFmZkZhg4disGDByM5ORl9+vRBSkqKVp7ly5cDAGbPno3hw4fD09MTNjY2yrWbk0+WNTJzjWbm+tQE4JpAKr/RXANqtVoJJtP703SD9vf3Nyq/hmZJrcePH+PMmTO52qYjR44gMjISJUqUwJYtW9CkSRO4uroqvRlEJN0bGP369QMArFixAsnJyYiLi8O6desA6J8dOy3NtZnejUwiImMwYCb6j0m7nE3a9S01TwAM/Qi9ePFihmWnDcDTSkpKUsbipe1urOlCZ+h1Fy9ezNaaoqVLl8bHH3+MFStWKF15//rrL60lTd7U8jGA4XY+ePBAGT+Z9vhkdE4eP35scMxgVtul2X9MTIzB/Wq6XxYpUiRTy/bklZiYGMTGxqJAgQJKD4u0kpOTlSWO9Jk/fz5+/PFHFChQAFu2bDE4RjW7+0mPo6OjEtAZuo4MpWdXcHAw7O3tce7cOaxYsUJrm2a8e4MGDfS+9t9//9Wbnp33neYaTa8bsGab5jPGGJo5D3LrOGZX+fLlYWFhgfj4eL3rYecUX19f5Xti3rx5ubYf4H/XT/Xq1fXO73D27Nl0h3BUqlQJtWrVwt27d7F161asX78esbGxqFevXoZDWzTn+b333stGC4iIGDATvVViYmLSfRoJ/G9SJBMTE63gWTOWztAP3AULFmS4/99//x137tzRSV+1ahUePXoEW1tbrTVXNZMe/fjjj8qkSGkZ09XPWNWqVVOe9N2+fVtJ1/xIS0hIyLF9GfLLL7/oHS83f/58AK8C/LTrJGvOyYkTJ7Rubmikd06y2q4KFSqgdOnSAIA5c+bozaNZ77dFixaZKjuvaNYOfvbsmdaawhrLly83+NR2165dGD58OMzNzfHbb7+hXLlyubIfY2jeL5rr5XU5+X5Jq0CBAsp6vFOnTtV6yqxps773fWRkpDJJ1evSBkeZvUY1192KFSv0Pg3+66+/cOHCBZiYmKBZs2ZGl+vn5wfg1fra+ZFarUarVq0AAN98802u7cfU1BQTJ04EACxevBibNm3KtX2ld/0AwKxZszIso2/fvgBefb5qumOnN9kX8KqHVUREBExNTdNda52IyBgMmIneIitXrkSVKlXw448/6jwdfPnyJZYtW4aRI0cCANq3bw9nZ2dle8OGDaFWq3Hq1CmtHykpKSmYO3cufv311wz3LyLo3r27VpfdgwcPYvTo0QCAzz77TKtr6YABA+Dg4IArV65gwIABWj+cQ0JCsHjx4nTHoL1u9+7dGDlyJE6fPq2VnpSUhK+//hovXryAWq3WmoBGc9PgyJEjeoOcnPT48WP07NkTz549U9K2bNmidJMfO3as1pO3ypUrw83NDffv38fo0aO1ApW1a9fi66+/NjgRk6ZdBw4cyNRTepVKhfHjxwN49aN8zZo1yrYXL14gMDAQR44cgZWVlXJe8zsHBwdUqVIFycnJ+Oyzz7RuWmzYsAFDhgzRO9HTxYsX0aVLFyQnJ2P+/Pnw9/fPlf0Ya+TIkTAzM0NoaCiCg4OV6yElJQXTpk3Drl27slx2RkaMGAG1Wo3Lly9j1apVSrrmyfL48eO1gp5Tp06hTZs2eruuA68mldN0lw4PD89UXbp27YqyZcsiNjYWXbt21boJcfz4cWXm6Y8//hilSpUyutxatWrB0dERERERudKVPCdMmTIFNjY2mD9/PsaMGaPTXTk+Ph4bN25UuipnVc+ePfHJJ59ARNCpUyeMHDkS165d08l38eJFpVt+VtSqVQvm5uY4ePAgFi1apKS/fPkSEyZMwK+//goLC4t0y+jWrRvs7OywdetW7N27F3Z2dujcuXO6r9m3bx9SU1NRp06dt6KXDBHlc7m7ahUR5aTvv/9eaz3QEiVKSPXq1cXT01NsbGyU9GrVquldj/nbb79V8ri4uIivr68UKFBATExMZMmSJRmuw/zFF1+Ik5OTWFhYiI+Pj1SoUEF5Tf369fWuQbxx40YxMzMTAGJvby/Vq1eXYsWKCQAZNmxYhusMp12HeePGjcr+nJyc5L333hMfHx9xdHQUAKJSqWTx4sVa5Tx9+lQKFiwoAKRgwYJSu3Zt8fPzk2HDhunsK701jY1Zh3nq1KliZWUlarVafH19pVSpUkp9u3TporUGs8aGDRuUdbUdHR3F19dXChcurNTH0PG5cuWKWFpaCgApUqSI1K1bV/z8/GTatGk6503fmsX9+/dX6la8eHGpXr26ODg4CAAxMzOTlStX6rzGmDWtDV1D6dGUa2j9YX3lv96mbdu2iampqQAQBwcHqVatmnKdNW3aVHr06KFzPWn2a2lpKXXr1jX4t2TJkmztR+R/ayFn1Ma5c+cqbXR2dpbq1auLi4uLANB6/2aGvnWY9dGsz1uhQgVJSUkREZGbN2+Kq6urcpyqVKmivO+LFSsmU6dONXhN9OvXTwCIqamp+Pj4iJ+fn/j5+SlrVKe3jvHJkyeV/Wo+bzw8PJT2165dW54+fZqp4yAiMnToUAEg8+fP17td81lgzJrUxqzDbGidaM21p+8zZ8eOHcpnmpmZmVSsWFFq1qwp7u7uyrWX0bk0RmpqqkyYMEHMzc21Pgt8fX2levXqUqJECSXd0dFRvvvuO63XG7PWtojI+PHjlXxFixYVX19f5bMmODjY4GdcWmk/r/r27Zth2zTvwxUrVhhzKIiI0sUnzERvkUGDBiEsLAzjx49H3bp18eLFC5w4cQJXr16Fk5MTWrdujaVLl+Lff/+Fi4uLzusDAwOxYsUKVKtWDc+fP8elS5fg4+ODHTt2ZDiBCgCUK1cOx44dQ+fOnXHnzh1ERUWhfPnymDx5Mnbu3Kl3jFr79u2xf/9+tGzZEiqVCufOnUOhQoWwaNEigzMRG1K/fn3MnTsX7du3R4ECBXDx4kWcPXsWdnZ26NatGw4cOKB039Owt7fHjh070Lp1a6hUKhw6dAjh4eE4efJkpvZtjDp16uDAgQNo1qwZoqKicOfOHVSuXBlz587F6tWr9Y7r7NixI7Zu3Yp69eohKSkJkZGRKF26NNatW4egoCCD+ypTpgy2bt2KRo0aISEhAQcOHEB4eLjRy+z89NNP+P3339G0aVPEx8fj5MmTsLW1xUcffYRjx47l6eRpWdG8eXPs3LkTDRs2RHJyMiIiIlCgQAFMmzYNW7duNfgkFHj1ZD292YWvX7+eI/sxxmeffYZt27bBz88PiYmJiIyMhLu7O3777TcEBgZmq+yMjB49GpaWlrhw4QLWrl0L4NWMyv/++y+6desGW1tbREZG4sWLFxg0aBCOHz+ud112je+++w4jRoxAqVKlcO7cOYSHhyM8PNyoZX68vb1x+vRpjBgxAiVLlsT58+dx8+ZNVK9eHd999x1CQ0Oz9ORQ83TamB41eaVp06aIjIzEF198gcqVK+P69es4duwYHj16hLp162LatGnYsWNHtvejUqkwefJkXL58GV9++SVq166NFy9e4NSpU4iMjISlpSW6dOmCZcuW4caNGwbXbc/I1KlTsXDhQlSuXBkPHjzA5cuX4e3tjfXr1ytdwzOStgt2Rt2x4+LisGnTJhQoUACdOnXKUp2JiNJSiWQwIJKIiIjoP6JLly5Yv349Dh48iFq1auV1dcgImzdvRrt27eDl5YVz586lm1dzo2bmzJlvzbASIsrfGDATERHRO+Pq1avw9PSEn59fjjyppdzXpk0b/Pnnn/j222/T7WkRHx+PMmXKwNraWnlKTkSUXcbPtkNERET0litTpgxCQkJw4cIFPH/+/K1Ya/xdFh4ejq1bt8LW1hYBAQHp5o2OjsaAAQPQuHFjBstElGP4hJmIiIiI8hV/f3/ExcXhxIkTSElJwVdffYUvv/wyr6tFRO8gBsxERERElK+oVCqYmJigRIkS6Nu3L8aPHw8TE85VS0RvHrtkExEREVG+wuc5RJRf8FYdERERERERkR4MmImIiIiIiIj0YMBMREREREREpAcDZiIiIiIiIiI9GDATERERERER6cGAmYiIiIiIiEgPBsxEREREREREejBgJiIiIiIiItKDATMRERERERGRHgyYiYiIiIiIiPRgwExERERERESkBwNmIiIiIiIiIj0YMBMRERERERHpwYCZiIiIiIiISI//AwclujwaiFenAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 990x780 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Re-run: Publication-style B/W forest plots (Matplotlib only) after kernel reset.\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from matplotlib.ticker import ScalarFormatter\n",
    "\n",
    "plt.rcParams.update({\n",
    "    \"font.size\": 10,\n",
    "    \"axes.labelsize\": 11,\n",
    "    \"xtick.labelsize\": 9,\n",
    "    \"ytick.labelsize\": 9,\n",
    "    \"figure.dpi\": 150\n",
    "})\n",
    "\n",
    "# Figure 1: Fine–Gray baseline model (all covariates)\n",
    "rows_baseline = [\n",
    "    {\"variable\": \"Statin at baseline (≤0 days)\", \"hr\": 0.531, \"lcl\": 0.329, \"ucl\": 0.857, \"p\": \"0.0096\"},\n",
    "    {\"variable\": \"Age (per unit)\",               \"hr\": 1.026, \"lcl\": 1.018, \"ucl\": 1.034, \"p\": \"<0.0001\"},\n",
    "    {\"variable\": \"Male\",                          \"hr\": 1.236, \"lcl\": 1.038, \"ucl\": 1.471, \"p\": \"0.017\"},\n",
    "    {\"variable\": \"BMI (per unit)\",               \"hr\": 1.007, \"lcl\": 1.003, \"ucl\": 1.010, \"p\": \"<0.001\"},\n",
    "    {\"variable\": \"HbA1c (per unit)\",             \"hr\": 1.151, \"lcl\": 1.089, \"ucl\": 1.217, \"p\": \"<0.0001\"},\n",
    "    {\"variable\": \"Creatinine (baseline)\",        \"hr\": 1.378, \"lcl\": 1.280, \"ucl\": 1.483, \"p\": \"<0.0001\"},\n",
    "    {\"variable\": \"Hypertension (baseline)\",      \"hr\": 1.426, \"lcl\": 1.169, \"ucl\": 1.739, \"p\": \"<0.001\"},\n",
    "    {\"variable\": \"CVD (baseline)\",               \"hr\": 1.914, \"lcl\": 1.573, \"ucl\": 2.329, \"p\": \"<0.0001\"},\n",
    "    {\"variable\": \"CKD conservative (baseline)\",  \"hr\": 5.223, \"lcl\": 4.026, \"ucl\": 6.777, \"p\": \"<0.0001\"},\n",
    "    {\"variable\": \"ACEi/ARB (baseline)\",          \"hr\": 1.175, \"lcl\": 0.843, \"ucl\": 1.638, \"p\": \"0.34\"},\n",
    "]\n",
    "df1 = pd.DataFrame(rows_baseline)\n",
    "df1['y'] = np.arange(len(df1))[::-1]\n",
    "\n",
    "fig1, ax1 = plt.subplots(figsize=(6.6, 5.2))\n",
    "xerr1 = np.vstack([df1[\"hr\"] - df1[\"lcl\"], df1[\"ucl\"] - df1[\"hr\"]])\n",
    "ax1.errorbar(df1[\"hr\"], df1[\"y\"], xerr=xerr1, fmt='o', capsize=3, color='black', lw=1.2, ms=4)\n",
    "ax1.axvline(1.0, linestyle='--', linewidth=1, color='black')\n",
    "\n",
    "ax1.set_xscale('log')\n",
    "ax1.set_yticks(df1[\"y\"])\n",
    "ax1.set_yticklabels(df1[\"variable\"])\n",
    "ax1.set_xlabel(\"Subdistribution Hazard Ratio (Fine–Gray)\")\n",
    "\n",
    "xmin1 = max(0.3, df1[\"lcl\"].min() * 0.9)\n",
    "xmax1 = min(10.0, df1[\"ucl\"].max() * 1.12)\n",
    "ax1.set_xlim(xmin1, xmax1)\n",
    "ticks1 = [0.3, 0.5, 0.75, 1.0, 1.25, 1.5, 2, 3, 5, 7, 10]\n",
    "ax1.set_xticks([t for t in ticks1 if xmin1 <= t <= xmax1])\n",
    "ax1.xaxis.set_major_formatter(ScalarFormatter())\n",
    "ax1.grid(False)\n",
    "\n",
    "for _, r in df1.iterrows():\n",
    "    txt = f\"{r.hr:.3f} [{r.lcl:.3f}; {r.ucl:.3f}]  p={r.p}\"\n",
    "    ax1.text(xmax1*1.02, r.y, txt, va='center', fontsize=9, color='black', clip_on=False)\n",
    "\n",
    "ax1.invert_yaxis()\n",
    "fig1.tight_layout()\n",
    "png1 = \"/mnt/data/fig_finegray_baseline_forest_bw.png\"\n",
    "pdf1 = \"/mnt/data/fig_finegray_baseline_forest_bw.pdf\"\n",
    "#fig1.savefig(png1, dpi=300, bbox_inches=\"tight\")\n",
    "#fig1.savefig(pdf1, dpi=300, bbox_inches=\"tight\")\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "7bfecd56-ac34-4395-96d6-f9f00ea5189b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "('/mnt/data/fig_finegray_baseline_forest_bw.png',\n",
       " '/mnt/data/fig_finegray_baseline_forest_bw.pdf',\n",
       " '/mnt/data/fig_finegray_newuser_lag_forest_bw.png',\n",
       " '/mnt/data/fig_finegray_newuser_lag_forest_bw.pdf')"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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MNJime7YKgOzYsUNJT09Pl+7duyvH3a1bt0RE5PHjx+Lh4SEuLi7yzz//KPmTkpIkJCREAIizs7NF9TL2eyoicvPmTQEg9evXNzrfmDFjBIDs2rXLbPkrV65UnpEsU6aM7N69W54+fSr79++XChUqCACZNWuW2TLS09OVLjkLFiwwmmfOnDni7Oysd97q06eP3Lx502zZWfWi3XJ0Xboy7l+dlJQUASCNGjUyW8bt27elcOHC4uLiYhCjlC5dWgAYfXhY141r3LhxBtNyqltOjr+h9ssvv8SGDRswduxY7Nq1y2S++fPnQ6vVYubMmXrdKtzc3DBmzBi0adMGGzZswJAhQ+Dk5IS6deti79690Gq1UKvViImJgZ+fHz744AN07doVFy9eRKlSpZRbX7rW/pzm6upqcNXs6uqqfE5PT8eCBQtQu3Zt9O/fXy9fjRo10L59e6xfvx6PHz/Wu7Pg4uKCL7/80mjXFCcnJ+X2l45KpULevHkzre+NGzcAAPny5ct85f4/Y7cWNRoNevfujY8++ghXrlxRbjWuWLECarUaEyZM0MvfrVs3fPXVVxa1nmd0+vRpvbJKliyJtWvXGoz3++TJE5Pr5OXlBQCZdsu6cuUK9u/fj8aNG+tddTs4OOCLL77Ajh07TM6bP39+HD16FE+ePFGWZ8rzdx10dMcs/U+LFi2MDkn3ujh79qxF+bp164YyZcq85NrYBq1WCz8/PwBAy5YtlfNvenq6NatFudzy5cuRlpZmdOhBlUqFGTNmoEOHDmjZsiU6deqEwoULIyYmBufOnUOxYsVw7do15Vj08vLCpEmTMGTIENSuXRudO3eGj48Ptm3bhri4OPj4+CApKelVr6JRumE8tVotVq1ahapVqwIA6tatizVr1qBSpUqYPn06Bg0aZLKMDz/8ENu2bUPPnj3Rr18/g+mjRo3Cd999h8mTJ6Nbt25wdXVFZGQkPvjgA+zYsQNHjx5VvtO5VXx8PNq3b4+bN29iwYIFNvdOghwP7suWLYuePXtiyZIl2Llzp9J36XkHDx6Eo6OjQTcPALh37x4A/R+6Ro0aYdeuXTh+/DiqV6+O3bt3o1GjRkoQHxMTg1KlSmH37t1QqVRo2LBhTq8aOnfujPnz5yM4OBidO3dGkyZNUL16db1g5OzZs3j8+DHS09ONvoTg1q1bEBGcP38ewcHBSnqJEiWQJ08eo8vcsGEDKlWqhB49eqBp06aoX7++3gWROf/99x8AGC3blKSkJEyfPh1r167F+fPnER8frzf933//VYL7kydPomDBgkYvCOrUqZPl4L5Tp04QESQmJuLMmTP44osv0KBBA6xatQrt2rXLUlmZOXnyJAAY9P8Dnj1D8nwfuox02/PBgweZBvdkub1791q7CrnC7du3cfv2bWtX45V70b7VlHvpzq/G+pLr0l6kC+6SJUugUqnQu3dvo9Pbt2+PyMhIfPXVV9i0aRO0Wi3q16+PPXv2oGfPnnBwcND7XR08eDAKFiyIb7/9FmvWrIGjoyOaNWuG6dOno1KlSvD3989yHTMytx0ypme2LXTTixYtqgT2OuXLl0epUqVw/vx5PHr0CD4+Pgbzjx49GrNnz8Zbb72FRYsWGUzftm0bpkyZgo8//hjDhw9X0jt27AgRQefOnfH999/jq6++MlvPly07x1ViYiLatm2Lv/76C1OnTjV6gZOx/OcbYrNz3Foqx4N74NnDqCtWrMDYsWNNBvcPHz5EWlqaQYtvRhmDyoxBfGBgII4fP46wsDDkz58fZcqUQUxMDPr06YOYmBhUqFBB6QOXk2bOnIlixYph0aJF+PTTTwEA/v7+GDZsGEaPHg2VSqU87Hb48GEcPnzYonUDTLesd+vWDWq1Gt999x2mTJmCyZMnw9XVFd26dcP06dONfvky0j0klJVWg/bt22P79u2oUaMGwsLCkDdvXjg4OOD48eP47bffkJycrOR9+vSpEug/Lyt3C4zVu3r16li3bh2qVq2Kvn374tq1a8r6eHl5Zfskp8tn7KSrVqvNtiwkJiYq9cyM7tmQ55lq0X+dNWjQ4LVvubckaC9QoMBr03JvSnp6Oi8GXwOBgYEAnt3prF69ut60ixcv6uWx1PHjx3Hy5Ek0adIExYoVM5kvNDTU4EWGKSkpuHDhAipUqGDwHFvHjh3RsWNHvbTr16/j6dOnaNy4cZbq+DwPDw/kz58fly9fVnowZGTptggKCgJg+vdRl56YmGgQX4wfPx7ffPMN3nzzTaxevdpoA9i2bdsAGO89oUs7fvy42Tq+ChmPq+eZ25bJycl4++23ERUVhQkTJuDjjz82Wf6RI0dw8eJFg+D+RY/brHgpwX1AQAD69++POXPm4LfffjOax9PTEy4uLkhISDD6cMjz6tSpA2dnZ8TExKBMmTLQarXKgdKoUSNs374dt27dwsWLF42+1c0Y3XKNjapiLHB0dnbG2LFjMXbsWFy6dAk7duzAjBkzMHbsWPj4+OCDDz6Ap6cnAKBfv35YsGCBRfUAYLQ7jk7Xrl3RtWtX3L9/H9HR0Vi4cCEWLVqEuLg4rFmzxmy5usBV14KfmYMHD2L79u3o378/5s+frzdt8uTJBvvT09NTudPyPN0IAtnh6OiIRo0aYe7cuYiNjUW1atUAPPtS/PXXX7h3755BcG7pF0d3ZW2s/lqtFg8ePDA578OHD6FSqXL9rUVb88cff8Dd3d3a1bCaqKgogwezjFm5cuVr/2bp+Ph4eHh4WLsa9JI1bNgQkydPRmRkJDp37qw3Tfcm0azeqdc9SPsibwP9/fffkZiYaFAXU1avXg0AFuc3p2HDhli3bh0OHjyIOnXqKOmJiYnYt28fKleunOmd5MDAQBQuXBiXLl1CcnIyXFxclGmpqam4ePEi3NzcDH7bpkyZggkTJiA0NBQbNmwwOUCHbiAKYyPO6dIsHdzjZdIdM5GRkRg5cqTeNFPHVWpqKrp06YJt27bhk08+wbhx48yWv3r1amVwkufLd3BwMNprIKfk6Gg5GX322Wdwc3PD559/bvRVzTVr1kRycrLFV3Curq6oVasW9uzZg+joaOTJkweVKlUC8Oxq8OrVq1i6dKnyf0vorkpv3bplMC2z11qXLFkSAwYMwPbt2wEAGzduBACUK1cOHh4eOHToUI6PaOHn54dOnTph8+bNKF26NDZv3pzpMsqXLw+VSmXxk/q6ISeNdYHZt2+fQVrlypXx77//Gh0l4PmhS1+UbpjRjK0EGb+Yz4uMjISvry/Kly9vttzKlSsDgNHhqA4ePIjU1FST8547dw5ly5Y123WHKKuMtRQay/OynimyRfHx8VCpVFCpVAZ3PMn+NWvWDEWKFMGyZcv0uurGxsZi+fLlqFatmtLoAzwLwGJjY3Ht2jWj5aWlpWHFihXw8PAwaGXPKC4uzuD39fLly/joo4+QP39+gz7pT58+NSjjyJEj+Oqrr1ChQgV06dLFovU159133wUARERE6D1zMnXqVDx58gR9+/bVy3///n3ExsbqPX+mUqnQv39/xMfHY9KkSXr5p02bhocPH6Jdu3Z6z/nNmjULo0aNQt26dbFx40azd6zr1q0LAPjuu+8QFxenpGu1Wnz99dcADGO00NBQi4fNfhHXrl1DbGys3m96hQoVULNmTezYscNgWNOZM2eiUKFCaNmypZKenp6Onj174vfff8eQIUP0RnQ0pkuXLnB3d8cPP/yg19AZExODnTt3ol27di+3cTBbj+OK4Wg5GY0cOVIAKK8HzjjCw5EjR0StVkudOnXk/v37BvOePn1a761eIv97Gtzf31/atWunpF+7dk1JByD//vuvRXW/fv26qFQqad68ud4IOLqReJBhpJekpCT5888/Dco4cuSIAJD27dsraUOHDhUA8vXXXxuMOpOammrwtlWYGZVlz549BqPzxMXFScGCBcXHx8ei9SxXrpwEBAQYnfb8aDl79+4VAPLpp5/q5Vu/fr3RkTp0r1d+99139fKvWrUqS6PlHD9+3GA9RZ49Ve7o6CiFCxfWm55TL7F6kdFybty4IQDkvffes2gZpnC0nGc46om+//77T0JDQ42OkhMaGpojb1/OTThajn2z5Dy4ceNGUavVkidPHhk0aJAMGjRI8uTJI66urvLXX3/p5dXFJKZ+U3///XcBIOHh4WaXuWzZMgkICJB3331XRo8eLT169BB3d3dxd3eXPXv2GOT/8ssvpXz58tK/f38ZPXq0dOzYUZycnCR//vxy5swZ8xshA5h5iZWISJcuXQSA1KhRQ0aNGiXt2rUTAFKnTh29N8KK/G/Uv0WLFumlx8fHS40aNQSANG3aVEaMGCFvvPGGAJCCBQvqvaAxMjJSVCqVqNVqGTZsmERERBj8ZfyNT0lJkdq1awsAKVy4sAwcOFCGDRsmlStXFgAGv9kiIo0aNRIAygiJmdmzZ4+EhYVJWFiY1K9fX1l/XdqGDRv08utGLHo+Fjl8+LBoNBrRaDTSt29fGT58uBQuXFhUKpX8+uuvennHjRsnAMTPz0/GjRtndDs8b86cOcp2GD58uPTt21fc3NzEz89PLl26ZHTdbO4NtcaC+wcPHoiXl5fJ4dumTZsmKpVK8ubNK927d5dRo0ZJeHi4BAcHC4y8hW3btm1KWd9++63eNN0bcoOCgrJU/44dOwoAqVu3rowYMULatWsnrq6uymuSdQfDw4cPBYBUqFBBevbsKZ9++qn06dNHfHx8xNHRUXbu3KmUGR8fL3Xq1BEAUqVKFRk4cKCMGDFCOnXqJAUKFJAyZcro1cHciahKlSpSsGBB6dSpk4wcOVKGDBmiXCyNHz/eonXUDUVq7GB6PrhPTU2VqlWrikqlkrZt28rIkSOlVatWolarpX379gb7MeMXuUGDBjJ69Gh55513xMXFRTlZXL16NdM6tm/fXgoVKiQdO3aUESNGyIcffihNmjQRAOLi4qL3CnCdTz75RNnnI0eOlO7du4uDg4MEBgZaHAAdPXpUNBqNODs7S+/evWXUqFFSpUoVZbsb2y9Lly4VALJx40aLlmEKg/tnGKQZ0mq1EhUVpfeG2qioKL6hlsG93bH0PLhr1y4JCQkRd3d38fT0lBYtWsiRI0cM8mUW3Ot+86Ojo80u7/jx49KqVSvJnz+/ODk5SaFChSQ8PFwuXrxoNH9kZKQ0btxYfH19xdnZWYoXLy5DhgwxaKTMTGbBfUpKikycOFECAwPF2dlZihQpIsOHD5enT58a5DUV3IuIPHnyRD7++GMpVqyYODk5SYECBaRv375y48YNvXy6oSbN/T0f28XFxcm4ceOkfPny4uLiIs7OzhIUFCSffPKJ0bfi+vn5Sa1atSzaPpbU6fnA2FRwL/K//ezt7S3u7u7SoEEDo8NjhoWFZbodjFm7dq3UrFlT3NzcJE+ePNKpUye5cOGCyXXLFcG9iMj48eNNHgAiz76wbdq0ET8/P3FycpIiRYpI06ZNZfbs2QYn7KdPn4qjo6MAkMOHD+tNCw8PFwDSr1+/LNX/6dOn0r9/f/H19RU3Nzdp2LCh7N+/32Cc+5SUFJk0aZI0adJEChYsqHyp3n77bTl48KBBuUlJSTJ16lSpVq2a8mrioKAgCQsL07sQEDEf3K9cuVI6dOggAQEB4urqKv7+/tKwYUNZvXq1xeuoGwv466+/NphmbJz7mzdvSrdu3SRfvnzi7u4udevWlU2bNpl8L8DDhw9l4MCB4u/vL25ublKnTh3ZuXOnDB48WABYFGhv3LhRunTpIiVKlBA3NzdxcXGRUqVKSb9+/Uy2emi1Wpk3b55UrFhRXFxcJF++fNKnT58sn0wPHTokjRs3Fjc3N/H19ZVevXrJvXv3jG4bEZGWLVtKoUKFJC0tLUvLeR6De6LMMYi3bzwP6sssuLc3sbGxAkBWrVpl7arYhJwK7lUifNXh66B169a4dOkSTp8+bfbh3ZwUEhKC06dPm3zgNje6efMmSpQogXHjxuGzzz7LVlm60XJMjaZDRPoPzsbFxb3WD1zbI54H9WX8fa5QoQL++ecfK9bm5fvpp58wYcIEXLp06bUeKe3777/XGzo0IiLC6HDqluLTgK+JiRMnonr16li/fj06deqUo2Xfvn0bBQoU0Etbt24ddu/erTwAZC+mTJmCvHnz6n0JiYiIckJERITyOTvDSecWffr0QZ8+faxdDaurU6eO3r7P7mhoDO5fE1WrVsWCBQuMDvuZXb169cKTJ09Qo0YNaDQa/P3339i+fTt8fX31DlZ7ULhwYSxZsoSth0RElOOy01pLuVedOnX0hjfNLnbLoWz78ccfsWDBApw/fx5Pnz6Fn58fmjdvjvHjx6NkyZLWrp7N4u1ooswlJSUpQxauX78erq6uVq4R5SSeB4lyHoN7IivhjxoRve54HiTKeS/tJVZERERERPRqMbgnIiIiIrITDO6JiMhmxcfHw93dHe7u7oiPj7d2dYiIbB5HyyEiIpuWkJBg7SoQEeUabLknIiIiIrITDO6JiIiIiOwEg3siIiIiIjvB4J6IiIiIyE4wuCciIiIishMcLYeIiGyWWq1GSEiI8pmIiMxjcE9ERDbLzc0N0dHR1q4GEVGuwWYQIiIiIiI7weCeiIiIiMhOMLgnIiKbFR8fD39/f/j7+yM+Pt7a1SEisnnsc09ERDbt/v371q4CEVGuwZZ7IiIiIiI7weCeiIiIiMhOMLgnIiIiIrITDO6JiIiIiOwEg3siIiIiIjvB0XKIiMhmqdVqBAcHK5+JiMg8BvdERGSz3NzccOjQIWtXg4go12AzCBERERGRnWBwT0RERERkJxjcExGRzUpISEBAQAACAgKQkJBg7eoQEdk89rknIiKbJSK4evWq8pmIiMxjyz0RERERkZ1gcE9EREREZCcY3BMRERER2QkG90REREREdoLBPRERERGRneBoOUREZLNUKhXKly+vfCYiIvMY3BMRkc3SaDQ4deqUtatBRJRrsFsOEREREZGdYHBPRERERGQnGNwTEZHNSkhIQIUKFVChQgUkJCRYuzpERDaPfe6JiMhmiQhOnz6tfCYiIvPYck9EREREZCcY3BMRERER2QkG90REREREdoLBPRERERGRnWBwT0RERERkJzhaDhER2SyVSoXixYsrn4mIyDwG90REZLM0Gg2uXLli7WoQEeUa7JZDRERERGQnGNwTEREREdkJBvdERGSzEhMTUbNmTdSsWROJiYnWrg4Rkc1jn3siIrJZWq0Whw8fVj4TEZF5bLknIiIiIrITDO6JiIiIiOwEg3siIiIiIjvB4J6IiIiIyE4wuCciIiIishMcLYeIiGyan5+ftatARJRrMLgnIiKb5e7ujnv37lm7GkREuQa75RARERER2QkG90REREREdoLBPRER2azExESEhoYiNDQUiYmJ1q4OEZHNY597IiKyWVqtFjExMcpnIiIyjy33RERERER2gsE9EREREZGdYHBPRERERGQnGNwTEREREdkJBvdERERERHaCo+UQEZFN02g01q4CEVGuweCeiIhslru7O+Lj461dDSKiXIPdcoiIiIiI7ASDeyIiIiIiO8HgnoiIbFZSUhJat26N1q1bIykpydrVISKyeexzT0RENis9PR1btmxRPhMRkXlsuSciIiIishMM7omIiIiI7ASDeyIiIiIiO8HgnoiIiIjITjC4JyIiIiKyEwzuiYiIiIjsBIfCJCIim+Xu7g4RsXY1iIhyDbbcExERERHZCQb3RERERER2gsE9ERHZrKSkJHTu3BmdO3dGUlKStatDRGTzGNwTEZHNSk9Px7p167Bu3Tqkp6dbuzpERDaPwT0RERERkZ1gcE9EREREZCcY3BMRERER2QkG90REREREdoLBPRERERGRnWBwT0RERERkJxytXQEiIiJTNBoN4uLilM9ERGQeg3siIrJZKpUK7u7u1q4GEVGuwW45RERERER2gsE9ERHZrOTkZISHhyM8PBzJycnWrg4Rkc1jcE9ERDYrLS0NS5YswZIlS5CWlmbt6hAR2TwG90REREREdoLBPRERERGRnWBwT0RERERkJxjcExERERHZCQb3RERERER2gsE9EREREZGd4BtqiYjIZmk0Gty9e1f5TERE5jG4JyIim6VSqeDv72/tahAR5RrslkNEREREZCcY3BMRkc1KTk7GoEGDMGjQICQnJ1u7OkRENk8lImLtShC9jipUqAAAOHXqlJVrQmS74uPj4eHhAQCIi4uDu7u7lWtEOYnnQaKcx5Z7IiIiIiI7weCeiIiIiMhOMLgnIiIiIrITDO6JiIiIiOwEg3siIiIiIjvB4J6IiIiIyE7wDbVERGSz3NzccPnyZeUzERGZx+CeiIhsllqtRkBAgLWrQUSUa7BbDhERERGRnWBwT0RENislJQUjR47EyJEjkZKSYu3qEBHZPJWIiLUrQfQ64mvXiTIXHx8PDw8PAEBcXBzc3d2tXCPKSTwPEuU8ttwTEREREdkJBvdERERERHaCwT0RERERkZ1gcE9EREREZCcY3BMRERER2QkG90REREREdoJvqCUiIpvl5uaGf/75R/lMRETmMbgnIiKbpVarlbHQiYgoc+yWQ0RERERkJ9hyT0RENislJQVff/01AGDMmDFwdna2co2IiGybSkTE2pUgeh3xtetEmYuPj4eHhwcAIC4uDu7u7lauEeUkngeJch675RARERER2QkG90REREREdoLBPRERERGRnWBwT0RERERkJxjcExERERHZCQb3RERERER2guPcExGRzXJ1dcXBgweVz0REZB6DeyIislkODg6oWbOmtatBRJRrsFsOEREREZGdYMs9ERHZrJSUFMyYMQMAMHToUDg7O1u5RkREtk0lImLtShC9jvjadaLMxcfHw8PDAwAQFxcHd3d3K9eIchLPg0Q5j91yiIiIiIjsBIN7IiIiIiI7weCeiIiIiMhOMLgnIiIiIrITDO6JiIiIiOwEg3siIiIiIjvBce6JiMhmubq6IioqSvlMRETmMbgnIiKb5eDggNDQUGtXg4go12C3HCIiIiIiO8GWeyIislmpqamYP38+AOC9996Dk5OTlWtERGTbGNwTEZHNSklJweDBgwEA4eHhDO6JiDLBbjlERERERHaCwT0RERERkZ1gcE9EREREZCcY3BMRERER2QkG90REREREdoLBPRERERGRneBQmEREZLNcXFywadMm5TMREZnH4J6IiGyWo6MjWrdube1qEBHlGuyWQ0RERERkJ9hyT0RENis1NRXLly8HAPTo0YNvqCUiygSDeyIislkpKSl49913AQCdO3dmcE9ElAl2yyEiIiIishMM7omIiIiI7ASDeyIiIiIiO8HgnoiIiIjITjC4JyIiIiKyEwzuiYiIiIjsBIfCJCIim+Xi4oI1a9Yon4mIyDwG90REZLMcHR3RuXNna1eDiCjXYLccIiIiIiI7wZZ7IiKyWWlpadiwYQMA4O2334ajI3+2iIjM4VmSiIhsVnJyMrp06QIAiIuLY3BPRJQJdsshIiIiIrITDO6JiIiIiOwEg3siIiIiIjvB4J6IiIiIyE4wuCciIiIishMM7omIiIiI7ATHFCMiIpvl7OyMRYsWKZ+JiMg8BvdERGSznJycEB4ebu1qEBHlGuyWQ0RERERkJ9hyT0RENistLQ3btm0DALz55pt8Qy0RUSZ4liQiIpuVnJyMNm3aAADi4uIY3BMRZYLdcoiIiIiI7ASDeyIiIiIiO8HgnoiIiIjITjC4JyIiIiKyEwzuiYiIiIjsBIN7IiIiIiI7wTHFiIjIZjk7O2PWrFnKZyIiMo/BPRER2SwnJycMGjTI2tUgIso12C2HiIiIiMhOsOWeiIhsVnp6Ovbs2QMAaNiwIRwcHKxcIyIi28bgnoiIbFZSUhIaN24MAIiLi4O7u7uVa0REZNvYLYeIiIiIyE4wuCciIiIishMM7omIiIiI7ASDeyIiIiIiO8HgnoiIiIjITjC4JyIiIiKyExwKk4iIbJaTkxOmTJmifCYiIvMY3BMRkc1ydnbGyJEjrV0NIqJcg91yiIiIiIjsBFvuiYjIZqWnp+Po0aMAgOrVq8PBwcHKNSIism0M7omIyGYlJSWhVq1aAIC4uDi4u7tbuUZERLaN3XKIiIiIiOwEg3siIiIiIjvB4J6IiIiIyE4wuCciIiIishMM7omIiIiI7ASDeyIiIiIiO8GhMImIyGY5OTkhIiJC+UxEROYxuCciIpvl7OyM8ePHW7saRES5BrvlEBERERHZCbbcExGRzdJqtThz5gwAoFy5clCr2SZFRGQOg3siIrJZiYmJqFixIgAgLi4O7u7uVq4REZFtYxMIEREREZGdYHBPRERERGQnGNwTEREREdkJBvdERERERHaCwT0RERERkZ1gcE9EREREZCc4FCYREdksJycnfPzxx8pnIiIyj8E9ERHZLGdnZ0ydOtXa1SAiyjXYLYeIiIiIyE6w5Z6IiGyWVqvFtWvXAADFihWDWs02KSIicxjcExGRzUpMTESJEiUAAHFxcXB3d7dyjYiIbBubQIiIiIiI7ASDeyIiIiIiO8HgnoiIiIjITjC4JyIiIiKyEwzuiYiIiIjsBIN7IiIiIiI7waEwiYjIZjk6OuKDDz5QPhMRkXk8UxIRkc1ycXHB7NmzrV0NIqJcg91yiIiIiIjsBFvuiYjIZokI7t+/DwDw8/ODSqWyco2IiGwbg3siIrJZCQkJyJcvHwAgLi4O7u7uVq4REZFtY7ccIiIiIiI7weCeiIiIiMhOMLgnIiIiIrITDO6JiIiIiOwEg3siIiIiIjvB4J6IiIiIyE5wKEwiIrJZjo6OCAsLUz4TEZF5PFMSEZHNcnFxweLFi61dDSKiXIPdcoiIiIiI7ARb7omIyGaJCBISEgAAGo0GKpXKyjUiIrJtbLknIiKblZCQAA8PD3h4eChBPhERmcbgnoiIiIjITjC4JyIiIiKyEwzuiYiIiIjsBIN7IiIiIiI7weCeiIiIiMhOMLgnIiIiIrITHOeeiIhsloODAzp16qR8JiIi8xjcExGRzXJ1dcXatWutXQ0iolyD3XKIiIiIiOwEg3siIiIiIjvB4J6IiGxWfHw8VCoVVCoV4uPjrV0dIiKbx+CeiIiIiMhOMLgnIiIiIrITDO6JiIiIiOwEg3siIiIiIjvB4J6IiIiIyE4wuCciIiIishN8Qy0REdksBwcHtGrVSvlMRETmMbgnIiKb5erqis2bN1u7GkREuQa75RARERER2QkG90REREREdoLBPRER2az4+Hi4u7vD3d0d8fHx1q4OEZHNY597IiKyaQkJCdauAhFRrsGWeyIiIiIiO8HgnoiIiIjITjC4JyIiIiKyEwzuiYiIiIjsBIN7IiIiIiI7wdFyiIjIZqnVaoSEhCifiYjIPAb3RERks9zc3BAdHW3tahAR5RpsBiEiIiIishMM7omIiIiI7ASDeyIislnx8fHw9/eHv78/4uPjrV0dIiKbxz73RERk0+7fv2/tKhAR5RpsuSciIiIishMM7omIiIiI7ASDeyIiIiIiO8HgnoiIiIjITjC4JyIiIiKyExwth4iIbJZarUZwcLDymYiIzGNwT0RENsvNzQ2HDh2ydjWIiHINNoMQEREREdkJBvdERERERHaCwT0REdmshIQEBAQEICAgAAkJCdauDhGRzWOfeyIislkigqtXryqfiYjIPLbcExERERHZCQb3RERERER2gsE9EREREZGdYHBPRERERGQnGNwTEREREdkJjpZDREQ2S6VSoXz58spnIiIyj8E9ERHZLI1Gg1OnTlm7GkREuQa75RARERER2QkG90REREREdoLBPRER2ayEhARUqFABFSpUQEJCgrWrQ0Rk89jnnoiIbJaI4PTp08pnIiIyjy33RERERER2gsE9EREREZGdYHBPRERERGQnGNwTEREREdkJBvdERERERHaCo+UQEZHNUqlUKF68uPKZiIjMY3BPREQ2S6PR4MqVK9auBhFRrsFuOUREREREdoLBPRERERGRnWBwT0RENisxMRE1a9ZEzZo1kZiYaO3qEBHZPPa5JyIim6XVanH48GHlMxERmceWeyIiIiIiO8HgnoiIiIjITjC4JyIiIrIBKpVK+atYsaK1q0OvyPfff6+378ePH5+t8hjcExERkU2Ljo5G48aN4enpCR8fH7Rs2RLHjh2zeP7Q0FC94On5PwcHB738Fy9exLhx49CyZUv4+flBpVLhnXfeMVq2iGDLli147733UKFCBXh4eMDT0xN169bFTz/9BBHJ0rpWqVIFERER+OCDDwympaWlYdKkSQgKCoKrqyuKFSuGESNGIC4uzuLyw8PDTW4HY+tobtt9+umnRpchIli6dCkaNmwIb29veHh4oEKFCkbX6UXcv38fAwYMQMGCBeHq6oqKFStizpw5WdrWqamp+Oabb1C+fHm4uLjAz88PnTt3xrlz54zm/+GHH9CrVy+ULVsWarUaKpUKSUlJRvMmJCRg2rRp6NKlC0qUKAGVSoUCBQqYrEudOnUQERGBsLAwi+tvDh+ofU0kJycjKCgI7du3xw8//KCkBwQEICAgANHR0darnI0ytm1q1KiBokWL4tdff7VavYiIXiebN29Gu3bt4O3trQQ/K1asQP369RETE4OaNWtmWkZ4eDhCQ0MN0mNjY7F69Wo0a9ZML33Pnj348ssv4ezsjMDAQDx48MBk2cnJyWjdujU0Gg2aNm2KNm3a4NGjR/jll1/Qt29f7N+/Hz/++KPF61u1alWTLbc9e/bE6tWrUaNGDQwdOhSxsbGYPn06Dhw4gOjoaDg7O1u8nKFDh8LHx0cvzdzdgoiICIO0Bg0aGKSlp6ejV69eWLlyJapVq4a+ffvCwcEBly5dwpo1azBnzhyL62jMo0eP0KBBA5w9exbt2rVD2bJlERkZiUGDBuHixYv49ttvMy1Dq9XirbfewpYtW1C2bFm8//77ePToEdasWYMdO3Zg//79KF++vN48Q4cOBQAUL14c3t7eePTokcny7969i5EjR0KlUqF06dJwdXU1W586deqgTp06iI6OxpIlSzLfCJmRbLp8+bIAEADy7rvvGs0zatQoASBRUVHZXRy9oG+//VacnZ3l+vXreunFixeXkJAQ61TqOXfu3JH3339fihQpIk5OTlK0aFEZNmyYPHr0yOQ88+bNk0qVKomrq6vkz59f+vbtK3fu3MmR+hjbNuvWrRMAcuDAgWyXX758eSlfvny2y8mquLg45TsbFxf3ypdPud+rPIbi4uLEz89P/Pz8eLzaoczOg8nJyVKkSBHRaDQSGxurpJ85c0Y0Go1Ur149W8sfOHCgAJDly5frpV+6dEn++usvSUpKUuKcrl27Gi0jJSVFJk2aJI8fP9ZLv3//vpQoUSJLvxkAJCwszOi0zZs3CwB54403JC0tTUmfMGGCAJAZM2ZYtIywsDABIJcvX7Yof0hIiGQlXJw0aZIAkGnTphlMS01NtbgcU0aOHCkA5IsvvtArt3nz5qJSqeT48eOZlrFy5UoBIKGhoZKUlKSkHzt2TJycnKRRo0YG82zZskXu3bsnIv/bJomJiUbLf/r0qezcuVOJX4oXLy758+fPtF5RUVECQCIiIjLNa06OdstZunQpzp49m5NFUg5ISUnBN998g06dOqFIkSLWro5Rd+7cQa1atTB37lwEBgbiww8/RJUqVfD9998jNDQU8fHxBvN8+umnGDhwIJKTkzFkyBA0adIEixcvRv369fHw4cOXUs8OHTqgePHi+OKLL15K+WR9IoKoqCj069cP7dq1Q79+/RAVFZXlW+uUM9zd3XHv3j3cu3cP7u7u1q4OvWI7duzAjRs30KtXL5QpU0ZJL1u2LHr06IGjR49mqXtORsnJyVi9ejW8vLzw9ttv600rUaIEatWqBRcXl0zLcXJywqeffgovLy+9dF9fX7z33nsAgL17975QHTNatGgRAGDChAl63YhGjhwJLy8vLFy4MNvLyK74+HhMmjQJoaGhGDFihMF0R0fDDiPXrl1DbGwsUlNTMy1fq9ViyZIl8Pb2xsiRI/XKHT9+PEQEP/30U6blbNy4EQAwatQovX1ctWpVvP3229i9e7dB9xxdFy1LeHh4oGnTpvD29rYof07LsW45JUqUwOXLlzFu3DisXr06p4qlHPDrr7/i3r17JvsL2oKIiAhcvXoVo0ePxtdff62kz5kzB4MGDcKUKVMwYcIEJf2ff/7B1KlTUalSJfz555/QaDQAgBYtWiAsLAxffvklpk+fnuP1VKlU6NKlC7799ltcv34dRYsWzfFlkPU8fPgQHTp0MOimtnDhQoSGhuKXX35Bnjx5rFM5otfQnj17AABNmzY1mNasWTMsWLAAe/bsQbVq1bJc9u+//46HDx+iX79+cHNzy3ZdjXFycgJgPKjNqj179sDDwwO1atXSS3dzc0P9+vWxdetWPHnyxOAiw5SNGzciLi4OGo0GwcHBqF+/vtn8y5cvx9WrV+Ht7Y169eoZ3ebbt2/HkydP0LFjRzx58gS//fYbbty4gUKFCqFly5bIly+fwTy9e/dGTEwMLl++jICAALN1OHfuHO7evYtWrVoZdHWpU6cOPDw8lGPGnLt37wJ41sXmebo6xMTEICgoKNOybFGOtdzXqlULLVu2xNq1a3HixAmL59uyZQuaNm0KHx8fuLm5ITg4GMuXL9fLM2nSJKhUKvz111966ZUqVYJKpcL//d//6aX36tULDg4OePz4sdllL168GCqVymh/8/Hjx0OlUuHKlStKWnp6OmbNmoVq1aopD4iUKlUK4eHh+Pfff/XmT0hIwBdffIHy5cvD1dUVvr6+Jh/UUKlUCA0NxeXLl9GpUyf4+vpCpVIp07du3YrGjRsjX758cHV1RZEiRdCuXTvs37/f7PrpLF26FBqNBm+++aZF+fft24d+/fqhTJkycHd3h5eXF0JCQrB582aj+Z88eYJBgwYhf/780Gg0qFu3LiIjI41uQ1M2btwItVqNzz77TC/9/fffR/78+bFw4UK9ltMlS5ZAq9VizJgxSmAPPDtJBAUFYenSpUhLS7NofY8cOYImTZrA3d0dfn5+CAsLw/37903m79ChA7RaLVasWGFR+ZQ7iIjRwF4nOjoaHTp0YAs+0St04cIFAECpUqUMpunSdHmySte3OTw8/MUqlwmtVouff/4ZANC4ceNslRUXF4c7d+6gRIkSUKsNQ7cX2RYffvghxowZg2HDhqFBgwaoXbs2rl27ZjJ/z549MXbsWAwePBjVq1dH69atDe6SHzlyBMCzhpIyZcqgd+/eGDNmDMLDw1GyZEmD+C6rzB0ParUaAQEBFm0DX19fAMDVq1cNpulilvPnz2ejptaVo91yJk6cCAAYO3asRfmnTZuG1q1b48KFC+jSpQv69++Px48fo2fPnpg8ebKSLyQkBACwe/duJe3Bgwc4deqUQbru/1WrVs3x2yEjR47EkCFDoFKp0LdvX7z//vuoVq0afv31V70DJCEhAY0bN0ZERATy5cuHQYMGoVWrVti4cSNq166N2NhYg7IfPHiABg0a4ObNmwgPD0e3bt0AAL/99htatWqF8+fPo2PHjhg+fDiaNGmCkydPGqy3MVqtVmnVsPRBm5kzZyIyMhI1a9bEkCFD0K1bN5w/fx5t27bFqlWr9PKmpaWhRYsWmDNnDgIDAzF06FAUL14crVq1woEDByxaHvDsKtrPz08vUAeeXfgUL14cN2/e1PvCmmvNadq0KR48eIDTp09nutxjx44hJCQE+/btQ6dOndCvXz8cP34czZo1Q0pKitF5qlevDmdnZ0RFRVm8fmT7oqOjM32wPDo6GjExMa+mQgQASExMRGhoKEJDQ5GYmGjt6tAr9uTJEwAw2hqtS8usIc+YO3fuYNu2bQgMDMy0xfpFTZo0CcePH0ePHj1QpUqVbJVlbjtkTLdkW4SEhGDDhg24efMmEhIScPLkSfTq1QsHDx5Eq1atDBrG3nrrLWzbtg23b99GXFwc/vrrL7Rs2RJbtmxRYhUdXcPYhAkTEBwcjNjYWDx69AirVq2Ck5MTwsPDcfz4cb15oqOjISKZttpbuh2ePn2a6dus33jjDQDA1KlT9X7rT548qQyY8SLHla3I0dFyqlWrho4dO2LdunU4cOAA6tatazLvyZMnMWrUKDRr1gy//fabEtQlJiaiWbNm+Oyzz9CjRw8UKVIENWvWhJubG2JiYpQ+Vnv27IGIICQkRO/H9sqVK7h27Ro6dOiQk6sG4FlLf3BwMP766y+9K+ekpCS9AykiIgIHDx7E/Pnz0b9/fyV92LBhqFu3LoYNG4Y//vhDr+x//vkHgwYNwsyZM/Va7ZcsWQJnZ2ccO3YM/v7+SrqIWNSv/PTp03jy5EmWbllOnjwZxYoV06vHd999h/r162PMmDF63XsWLlyIAwcOICwsDIsXL1bSV65cie7du1u8TF9fX9y7dw8JCQl6Ab6IKC0J58+fR+nSpQE8u3r39PTU2yY6GVswKleubHa5gwcPRnx8PCIjI9GkSRMAzy5SW7ZsiRMnThi9Jefs7IyKFStafOekQoUKRtMvXrxotPXhVWrRooXBEHCvK0ufF+rWrZte39/XUXp6+itbllarVc7xmf1gE1lq+fLlSEtLy7GhB5+3Zs0ajBs3DhUqVMDs2bNfyjJe1Lvvvqv3/0qVKmHp0qVISEjA+vXr8csvv6BLly7K9GHDhunlr1WrFjZu3Ij69etj27ZtOHjwoNJVSPcdzZ8/P9asWaN0d+ratSsePXqEgQMHYubMmVZ/PqBnz5748ccfsWvXLlStWhVvvvmmMlpOmTJl8Pfffxu9Q5Jb5PhQmF9++SU2bNiAsWPHYteuXSbzzZ8/H1qtFjNnztQL5tzc3DBmzBi0adMGGzZswJAhQ+Dk5IS6deti79690Gq1UKvViImJgZ+fHz744AN07dpVCZR0PwK61v6c5urqarDDM/b7Sk9Px4IFC1C7dm29wB54Noxi+/btsX79ejx+/FjvzoKLiwu+/PJLvYBax8nJSem3p6NSqZA3b95M63vjxg0AMNrPzRRjfdA0Gg169+6Njz76CFeuXFGusFesWAG1Wq3XHx54FgB99dVXFrWeA8+uopctW4avv/4aX331lZI+f/583L59G4D+VfSTJ09MrpOlLRhXrlzB/v370bhxYyWwBwAHBwd88cUX2LFjh8l58+fPj6NHj2apf6MtyomHvF43t2/fVo5JInq5dOdXXYttRrq0F7lLv2TJEqhUKvTu3Tt7FTRi48aN6NmzJ0qWLIkdO3bkSC8Cc9shY3p2ltWnTx+sX78ef/75p15wb4yDgwN69+6Nv/76C3/++acS3OuW36xZM4PnGNq2bYuBAwcqXXdehCXbwdPTM9PA3NnZGTt27MAXX3yBtWvXYvbs2ShUqBBGjx6NoKAgdO3a1WjjYW6R48F92bJl0bNnTyxZsgQ7d+40GDtW5+DBg3B0dDTo5gEA9+7dA6DfktaoUSPs2rULx48fR/Xq1bF79240atRICeJjYmJQqlQp7N69GyqVCg0bNszpVUPnzp0xf/58BAcHo3PnzmjSpAmqV6+u1/J59uxZPH78GOnp6UbHqb116xZEBOfPn0dwcLCSXqJECaMP6nXu3BkbNmxApUqV0KNHDzRt2hT169c36L5iyn///QcAWXoIMCkpCdOnT8fatWtx/vx5g5Fq/v33XyW4P3nyJAoWLGj0gqBOnToWB/dffPEFtm7diokTJ2L//v2oUaMGzp49i02bNqFixYr4559/cvwq+uTJkwCAevXqGUyrVauW2QegdNvzwYMHmQb3uu5jzzPVov8qNWjQgC33/9/Zs2ctCtoLFCjAlvv0dF4Y0isRGBgI4NmdzurVq+tNu3jxol4eSx0/fhwnT55EkyZNUKxYsZyp6P+3bds2dO7cGYUKFUJkZCQKFiyYI+V6eHggf/78uHz5stLImdGLbouMdCPBGBudztL8urvdxi4ydGnZ6V6X8Xh4nlarxZUrV5Q7/Jlxd3fH5MmT9bqBA88aqQG80EPatuKlvMRq/PjxWLFiBcaOHWsyuH/48CHS0tIMWnwzynjAZAziAwMDcfz4cYSFhSF//vwoU6YMYmJi0KdPH8TExKBChQrKwxI5aebMmShWrBgWLVqkvJXN398fw4YNw+jRo6FSqZSuMocPH8bhw4ctWjfAdMt6t27doFar8d1332HKlCmYPHkyXF1d0a1bN0yfPt3gBRTP0105m3qLmjHt27fH9u3bUaNGDYSFhSFv3rxwcHDA8ePH8dtvvyE5OVnJ+/TpU5P95LJytyAgIAAHDx7E559/ju3bt2Pfvn0oV64c1q5di5iYGPzzzz96V9FeXl7ZbsHQ5TN2da5Wq80OeaU7Ob2sERZelT/++IPDC/5/UVFRendwTFm5cqXRl+G8TuLj4+Hh4WHtatBroGHDhpg8eTIiIyPRuXNnvWk7d+5U8mSF7kHanO6SEx0djbfffhu+vr6IjIzM8QuHhg0bYt26dTh48CDq1KmjpCcmJmLfvn2oXLlytu4k62IWY411lubXnRvPnDljkF+Xlp3tEhQUhHz58mHfvn1ISkrS6znx559/Ii4uLluNuyKCtWvXwsvLS+mXnxu9lA5FAQEB6N+/Pw4ePIjffvvNaB5PT0+4uLggPT0dImL0TzemK/CsFdjZ2RkxMTFK9xxdwN+oUSPExMTg1q1buHjxIho1amRRPXVXvsZGVTEWODo7O2Ps2LG4cOECLl68iHnz5sHPzw9jx47F3LlzlfUCgH79+plcL92zAhkZ646j07VrV/z555+4e/cu1q5di9DQUCxatEgZP9ccXeCqa8HPzMGDB7F9+3b0798fhw8fxuzZs/Hll19i/PjxRp+h8PT0VO60PE831JSlSpQogZ9//hl3795FcnIyjh8/jo4dO+LUqVNQqVSoWrWqkjcwMBBPnz41umxLWzB0J0FjZWi1WrNvJHz48CFUKpXFY96S7dM9tJlZnpfV5Y+IDDVr1gxFihTBsmXL9O7mx8bGYvny5ahWrZpeC2tqaipiY2NNjvqSlpaGFStWwMPDAx07dsyxev75559o27YtPD09ERkZ+VKep9L1lY+IiNB77mXq1Kl48uQJ+vbtq5f//v37iI2N1eui+t9//+H69esGZcfGxiIiIgIODg562+XGjRtGfwv37duHmTNnwsvLCy1atFDSS5cujdDQUERGRuoNOpGamqr0Znh+u4eGhlo8sp5arUZYWBgeP36MqVOnKum6xmKVSmXwTIGpcfSfj/NEBBEREfj7778xatSoXN3w9VJa7gHgs88+w6JFi/D555/r7XidmjVr4tixY0o3m8y4urqiVq1a2LNnD4KCgpAnTx5UqlQJwLNW/QULFmDp0qXK/y2ha/W+deuWwbTMXopRsmRJDBgwAK1bt0bRokWxceNGfPDBByhXrhw8PDxw6NAhiIjZoD2r/Pz80KlTJ3To0AFly5bF5s2bM11G+fLloVKpLB7S6dKlSwCAdu3aGUzbt2+fQVrlypWxd+9eXL161eBq//mhS1/ErVu3sGfPHjRp0kTvGYOGDRvir7/+QmRkpMH4/ZGRkfD19TV4dbSxugMw+mDswYMHzb5Q49y5cyhbtmyOjF1MtkGlUuGXX34xORymbpz7nPxOE5F5Li4umDt3Ltq3b4+6desqAzWsWLECWq0W8+bN08t/8+ZNlCtXDiEhIUa/x1u3bsXdu3cRHh5uNni7f/8+Pv74YwDPhqEEnv2m6YbNLFu2rHIH/7///kPLli0RFxeHN99802h3Y0saDzLTqlUrdOnSBWvWrEHt2rXRrFkznDlzBr///jvq1KmDgQMH6uWfNWsWJkyYgEWLFin1vnbtGmrWrIkGDRqgdOnSyJMnDy5cuIBNmzYhJSUFU6dO1et2ePjwYXTr1g0hISEoWbIk3N3dcfr0aWzduhUODg5YtGiRwfN/c+fORb169fDmm2+iY8eOKFiwICIjI3Hy5Ek0btwY/fr108uvG17Y0t/TMWPG4LfffsO4ceNw5MgRlC1bFjt37sSRI0fw0Ucf6TUEAqbH0a9duzYCAgJQrlw5qNVqREZGKo2Kn3zyicFyv/nmG2W0Q92//fv3V7q2Tps2Ta/B7+OPP1ZGD7p//z7S09P1hl3NOAhJjsvW+21FzL6WWfeK4JIlSwoAiYqKUqYdOXJE1Gq11KlTR+7fv28w7+nTp+XOnTt6aWPGjBEA4u/vL+3atVPSr127pqQDkH///deiul+/fl1UKpU0b95c0tPTlfQNGzYor1bXvZ45KSlJ/vzzT4Myjhw5IgCkffv2StrQoUMFgHz99dei1Wr18qempsrevXv10gBISEiI0Tru2bNHr24iz17HXrBgQfHx8bFoPcuVKycBAQFGpxUvXlxv2Xv37hUA8umnn+rlW79+vbJNMu7HuXPnCgB599139fKvWrXKYBuak5KSYvAa5/j4eHnjjTdEpVLJnj179Kb9/fffolarpVKlShIfH6+kL1myRADI8OHDM12miEi9evUEgERGRippaWlp0rx5c5P75caNGwJA3nvvPYuWYUpmr11/WeLi4pR9ExcX98qXb+u0Wq1ERUVJ3759pW3bttK3b1+Jiooy+C6/zl7lMRQXFycajUY0Gg2PVztk6Xlw165dEhISIu7u7uLp6SktWrSQI0eOGOTTxSSmflM7duwoACQ6Otrs8nTlmPrLWH5meQFIREREpuso8iweCAsLMzk9JSVFJk6cKIGBgeLs7CxFihSR4cOHy9OnTw3yRkRECABZtGiRknbnzh0ZMGCAVKlSRfLkySOOjo6SL18+adeunezcudOgjHPnzklYWJiUK1dOvL29xcnJSQoXLizvvPOOHD582GQ9L1y4IO+88474+fmJs7OzlC5dWsaPHy9JSUkGef38/KRWrVrmN8xz7t27J/3795f8+fOLi4uLlC9fXmbNmmX0PB0SEmI0Fhk3bpxUqFBB3N3dxd3dXWrVqiXz5883ea7XlWPq7/nyixcvbja/MVFRUVk6Xkx5qcH9gwcPxMvLy2hQKCIybdo0UalUkjdvXunevbuMGjVKwsPDJTg4WADIgQMH9PJv27ZNKevbb7/Vm1aiRAkBIEFBQVmqv+6LXrduXRkxYoS0a9dOXF1dpWXLlno76+HDhwJAKlSoID179pRPP/1U+vTpIz4+PuLo6Kj3pYiPj5c6deoIAKlSpYoMHDhQRowYIZ06dZICBQpImTJl9Opg7kRUpUoVKViwoHTq1ElGjhwpQ4YMUS6Wxo8fb9E6jho1SgDIpUuXDKY9H9ynpqZK1apVRaVSSdu2bWXkyJHSqlUrUavV0r59e4P9mJKSIrVr1xYA0qBBAxk9erS888474uLiIm+88YYAkKtXr2Zax+vXr4u3t7d07dpVRo0aJe+//74ULlxYAMj06dONzvPJJ58o+3zkyJHSvXt3cXBwkMDAQPnvv/8s2jZHjx4VjUYjzs7O0rt3bxk1apRUqVJF2e7G9svSpUsFgGzcuNGiZZhireCeiMhW8DyoL7Pg3t7ExsYKAFm1apW1q2ITckVwLyIyfvx4k8G9yLOr8TZt2oifn584OTlJkSJFpGnTpjJ79myDVpqnT5+Ko6OjADC4YgwPDxcA0q9fvyzV/+nTp9K/f3/x9fUVNzc3adiwoezfv1+54tUF9ykpKTJp0iRp0qSJFCxYULlifvvtt+XgwYMG5SYlJcnUqVOlWrVqotFoxN3dXYKCgiQsLMzg6thccL9y5Urp0KGDBAQEiKurq/j7+0vDhg1l9erVFq/juXPnlDsJz3s+uBcRuXnzpnTr1k3y5csn7u7uUrduXdm0aZMsWrTI6H58+PChDBw4UPz9/cXNzU3q1KkjO3fulMGDBwsAiwLtJ0+eSM+ePaV48eLi4uIiefLkkZYtW8quXbtMzqPVamXevHlSsWJFcXFxkXz58kmfPn0M7vhk5tChQ9K4cWNxc3MTX19f6dWrl9y7d8/othERadmypRQqVEjS0tKytJzn8UeNiF53PA/qy9iyW6FCBWtX56VbuHChFCtWLNu/p7ndd99990J3ekxRifBd6q+D1q1b49KlSzh9+vQr6zMcEhKC06dPm3zgNje6efMmSpQogXHjxuGzzz7LVlm6oTBNDZVJRGTveB7Ul3EI7Xz58uGDDz6wXmXolfnzzz/1Xm6a3Wc0GNy/JnQPLq9ZswadOnXK0bJv376NAgUK6KWtW7cOnTt3xrvvvouffvopR5dnTUOHDsXq1atx8eLFbD9Jzx81oswlJSUpo2usX79eb+g7yv14HiTKeRzq4zVRtWpVLFiwwOiwn9nVq1cvPHnyBDVq1IBGo8Hff/+N7du3w9fXFxERETm+PGsqXLgwlixZkquHyCLKTdLT07FlyxblMxERmceWe8q2H3/8EQsWLMD58+fx9OlT+Pn5oXnz5hg/fjxKlixp7erZLLZYEWUu4wuz4uLieGFtZ3geJMp5bLmnbOvXr5/BuLVERERE9Oq9lDfUEhERERHRq8fgnoiIiIjITjC4JyIiIiKyE+xzT2Ql165dQ2pqqvJAGREZ0mq1yufg4GCo1WyTsicXL16Ek5OTtatBZFd4liSyEnd3d7v8UdNqtbh3755eUGbrcrrOFy9exMWLF3OkLFvZntaqh1qthouLC1xcXF77wD4njytbkZqaisTERGtXg8iucChMIspRV65cQYkSJXD58mUEBARYuzoWyek65+TwfrayPa1ZDw6X+Iw9bgd7XCcia3u9m0GIiIiIiOwIg3siIiIiIjvB4J6IcpSPjw8iIiLg4+Nj7apYzJbrbCt1s5V6EBGReexzT0SUw9iPOGdxez5jj9vBHteJyNrYck9EREREZCfYck9EREREZCfYck9EREREZCcY3BMRERER2QkG90REREREdoLBPRHZlVWrVqFhw4bw8vKCSqWydnWIiIheKT5QS0R2Zdu2bfjvv/+QmJiIvn37gqc4IiJ6nTC4JyK7FB0djcaNGzO4JyKi1wq75RDZgbS0NMyYMQPBwcHw8PCAt7c3qlWrhgkTJryyOvzwww/o1asXypYtC7VaDZVKhaSkJLPz6AJwT09P+Pj4oGXLljh27NgrqjEREZH9Ycs9US6XkJCA1q1bIzo6Gg0bNkSdOnWQmpqK8+fP4/r16zhx4sQrqYeuf3vx4sXx+PFjPHr0CImJiXB1dTWaf/PmzWjXrh28vb3RvXt3AMCKFSuQlJSEmJgY1KxZU8kbHh6OJUuWmFz2gAEDMG/ePL00ttwTEdFrSYgoVxswYICo1WpZvXq1wbTU1NRM5587d648fvzY5PSjR4/K9u3bMy1ny5Ytcu/ePRERCQkJEQCSmJhoNG9ycrIUKVJENBqNxMbGKulnzpwRjUYj1atX18v/9OlTuXfvnsm/uLg4g2VERUXJi5zioqKiJDQ0VDw8PMTb21tatGghR48etWhe3Xqb+lOr1Xr5Fy1aZDJv/vz5s1x3WzNjxgzp2bOnlClTRlQqldljIjPz5s2TSpUqiaurq+TPn1/69u0rd+7cMZo3NTVVvv76ayldurS4uLhI0aJF5aOPPpKnT59mZ3VeWE5sh6NHj8ro0aMlODhY8ubNKy4uLlK2bFn57LPPjB7/L/vYyol10n1HTf0ZK8/W9i2RLXJ8BdcPRPSSXL16FQsWLEDv3r3RpUsXg+mOjua/4sePH8cHH3yA5cuXY9u2bdBoNHrTT506hTfeeAMajQYXLlyAk5OTybJatmxpcb137NiBGzduYMCAAShTpoySXrZsWfTo0QMLFizAsWPHUK1aNQCAh4cHPDw8LC7/RWW8mxAWFgbg2d2E+vXrG9xNMCY8PByhoaEG6bGxsVi9ejWaNWtmdL727dujatWqemmvYn1ftqFDhwJ4djfH29sbjx49eqFyPv30U0yePBlBQUEYMmQIbty4gcWLFyMmJgYHDx5Enjx59PL37NkTq1evRo0aNTB06FDExsZi+vTpOHDgAKKjo+Hs7JzdVcuSnNgO77//Pg4dOoR69eqhd+/eAIDt27fjq6++wsaNG7F3716jx8zLOrZyat8CQEhIiNHvjbHzl63tWyKbZO2rCyJ6cdOnTxcAsnHjRrl7964sWLBAJk2aJCtXrjTbGp/R999/LwCkWbNmkpSUpKSfO3dOChQoIF5eXnLo0KEs1SuzlvtRo0YJAFmzZo3BtNWrVwsAmTFjRpaWqZOWliaJiYmybds2pQ6JiYmi1WrNzpfVuwlZMXDgQAEgy5cv10vXta4uWrTohcu2ZVm5m2PK33//LWq1WipVqiTx8fFK+pIlSwSADB8+XC//5s2bBYC88cYbkpaWpqRPmDAhW8dVduTEdpg1a5ZcvnxZLy01NVU6dOggAGTSpEl60172sZUT66RruY+IiLAovy3uWyJbxOCeKBfr0aOHAJBp06aJl5eX3i1tX19f2blzp0XlfPnllwJA2rRpIykpKXLlyhUpVqyYuLm5ye7du7Ncr8x+7Dt27CgA5MiRIwbTDh8+LABkyJAhWV6uiOnuCM8HRs/btGmTAJABAwYYTOvfv78AsLh7TkZJSUmSJ08e8fLykoSEBKN1tdfgPqMXDQA//vhjASArV640mBYUFCS+vr563c86deokAOTAgQN6eRMSEsTLy0sqV678YiuQQ150O5hy4MABASCtW7fWS3+Vx9arCu5tfd8S2QqOlkOUi92/fx8AMGrUKHTu3BlXrlzBgwcPMGfOHMTFxaFDhw64detWpuV89tlnGDlyJDZt2oQuXbqgadOmuH37NjZs2ICGDRvmeL2fPHkCAPDy8jKYpkt7/PjxC5UdHh4OedZwofcXEBBgdr49e/YAAJo2bWowTdedRpcnK37//Xc8fPgQXbp0gZubm9E8R48exdSpUzF16lRs3Lgx01GGXifm9kvTpk3x4MEDnD59Wi+/h4cHatWqpZfXzc0N9evXx8mTJ5Xjzx7ousqZ6oKXG46tc+fO4bvvvsPkyZOxbt06k/vnddu3RC+Kfe6JcjGtVgsAqFKlChYsWKCMWPP+++/j2rVr+Oabb7Bw4UJ8/vnnmZY1ZcoUPHz4ED/++CMAYO3atXjzzTdfXuVtzIULFwAApUqVMpimS9PlyQrdKD/h4eEm88ycOVPv/wULFsTy5cvRuHHjLC/P3ly4cAGenp7w9/c3mJZxv1SuXBlxcXG4c+cOKlWqBLXasO0qY/7q1au/3Iq/IkuXLgUANGnSxOj03HBsrVy5EitXrlT+7+Pjg3nz5qFr165K2uu4b4leFFvuiXIxb29vAECbNm2UwF6nbdu2AIAjR45YVNbjx49x9OhR5f8xMTE5VEtDutZ5Y61sujTdur0qL+Nuwp07d7Bt2zYEBgaifv36BtNLliyJefPm4eLFi0hMTMTFixfx1Vdf4eHDh2jbtu0LXUzYmydPnhjdJ4DhfjG3D43lz+1iYmIwZ84cBAYGon///nrTcsOx5e/vj2nTpiE2NhYJCQm4du0a5syZAwDo0aMH9u7dq+R93fYtUXYwuCfKxYKCggAYD4R1aYmJiZmWEx8fj1atWuHYsWOYP38+3nnnHcyaNQujR4/O2Qr/f4GBgQCAixcvGkzTpeny5GbLly9HWlqaMvLO8xo1aoQBAwagZMmScHV1RcmSJTF27FhMmzYN8fHxmD59+iuuMeUWsbGx6NSpE9zc3LBmzRqDLl+54diqUKECRowYgTJlysDNzQ1FixbF+++/j59//hnp6en4+uuvrV1FolyJwT1RLqYbPu7MmTMG03RpxYoVM1tGUlIS2rdvj/379+OHH35A//79sWzZMrRv3x7ffPMNvvrqqxyvt64ff2RkpMG0nTt36uV5VV7G3YQlS5ZApVIpQxdaKjw8HCqVCn/++WeW5rNHXl5eJvtRP79fzO1DY/lzq0uXLqFp06ZISEjApk2blCFjLZEbjq3WrVvD399fr46vy74lygkM7olyscaNG6N06dJYvny5XoAfFxeHb775BgDQsWNHk/Onpqaic+fOiIyMxOTJkzF48GAAzx7OW7NmDd588018/vnnmDFjRo7Wu1mzZihSpAiWLVuGs2fPKumxsbFYvnw5qlWrlqWAJSfk9N2E48eP4+TJk2jcuHGmF1jP02g0cHNzQ3x8fJbms0eBgYF4+vQp7t27ZzDt+f3i4eGB/Pnz4/Lly8rzKOby50ZXr15F48aN8eDBA/z6669o1KhRlubPLceWn5+fXh1fh31LlFMY3BPlYo6Ojvjxxx+h1WpRu3ZtvPvuu/jwww9RpUoVHDlyBD179kSLFi1Mzn/u3DlERUVh3Lhx+OSTT/SmOTs7Y8OGDWjUqBF++uknpKammq3LN998g/DwcISHhyM2NhYA0L9/fyVNN7IPALi4uGDu3LlISkpC3bp1MXjwYAwePBj16tWDVqvFvHnzsrFVXkxO303QPUhrqkuOOWfOnEFCQgKKFy+e5Xntjbn9EhkZCV9fX5QvX14vf1xcHA4ePKiXNzExEfv27UPlypVN9tu2dbdu3ULTpk1x69YtrFmzBs2bN89yGbnh2Hr06BEuXLhgUEd73rdEOcqqA3ESUY44fPiwtG7dWry9vcXFxUUqVaokM2bMkPT09EznPX/+vNnpT548kdu3b2dajm6sa1N/xsaZ37Vrl4SEhIi7u7t4enpKixYtjI59/yokJSWZfYlVtWrVlLSUlBQ5c+aMXL161WhZqampki9fPvHw8JC4uDiTyzx27JhB2sOHD6VBgwYCQH788ccXXyEbk9lY6Ka2qT28xCqjF90Od+/elXLlyomDg4OsXr060+W8ymPrRdfJWB0TExOlc+fOAkA+++wzvWm2vm+JbIVKRORVX1AQEdmiTZs2oX379vD29kb37t0BACtWrEBiYiJiYmKU8bWvXLmCEiVKICQkBNHR0QblbNy4Ee3atUN4eDgWLVpkcnl58uRB8eLFUblyZRQqVAj//vsvtm7dinv37qF9+/ZYv349HBwcXsq6vgrffPONchfnjz/+wJ07d9CzZ09lnaZNmwY/Pz8A5rfpqFGjMGXKFAQFBaF9+/a4efMmVq9ejRIlSuDgwYPIkyePXv6uXbtizZo1qFGjBpo1a4YzZ87g999/R506dRATEwNnZ+eXv/IZ5MR2CAkJwe7du1GjRg20adPGYBkBAQF6w62+7GMrJ9apatWqSEhIQM2aNVGkSBE8ePAAO3fuxNWrV1G7dm1ERkbC3d1db7m2tm+JbJK1ry6IiGyJJXcTLl++LAAkJCTEaBm6N/BGR0ebXdbnn38u9evXl/z584uTk5N4e3tL/fr1Zf78+RbddbF1WbmbY26barVamTdvnlSsWFFcXFwkX7580qdPH7lz547R5aakpMjEiRMlMDBQnJ2dpUiRIjJ8+HB5+vTpS1pT83JiOxQvXtxsGc/nf9nHVk6s04wZM6RJkyZSsGBBcXZ2Fg8PDwkODpYpU6ZIUlKS0eXa2r4lskVsuSciIiIishN8oJaIiIiIyE4wuCciIiIishMM7omIiIiI7ASDeyIiIiIiO8HgnoiIiIjITjC4JyIiIiKyEwzuiYiIiIjsBIN7IiIiIiI7weCeiIiIiMhOMLgnIiIiIrITDO6JiIiIiOwEg3uyW6GhoVCpVFi8eHGOlx0QEACVSoUrV67opYeHh7+0Zb6IK1euQKVSISAgwGCabh2io6Nfeb3M0e03W6sXvTiVSgWVSmXtauSI6OhoqFQqhIaGvtLlarVaVKlSBb6+vnj69OkLl2Ot+pNphw8fhkqlwrvvvmvtqpCdYHBPr8zt27cxduxYVK9eHV5eXnB2dkbBggVRtWpV9OvXD8uWLUNcXJy1q2kToqOjMX78ePz666/WrkqO+vXXXzF+/Hi7CNzHjx9vcZCkC27tYb1flcWLFyvbLeOfp6cnKlasiKFDhxpcXGfXo0ePMH78eIwfPz5Hy80JCxcuxMmTJ/Hxxx/D09NTb5quUcHcn4+Pj3Uq/pLcuHEDERERaNCgAQoUKABnZ2d4enoiKCgIXbt2xdKlS7N1EfQqBQcHo23btli6dClOnDhh7eqQHXC0dgXo9bB37160bdsWjx49gkqlQuHChVG2bFnEx8fjzJkzOHHiBBYuXIg9e/agQYMG1q7uCytYsCDKlCkDb2/vbJUTHR2NCRMmICwsDG+99dYLl+Pk5IQyZcqgcOHC2apPTvn111+xZMkSADAZFBcrVgxlypSBRqN5hTUjW+Xi4oLg4GAAgIjg5s2bOHPmDE6dOoWffvoJW7duzbFzxqNHjzBhwgQAMBngazQalClTBsWKFcuRZVoiMTER48aNg4+PD4YMGWIyX758+VC6dGmj03QXBNaof04SEXz55ZeYOHEiUlJSAABFixZFlSpVkJycjOvXr2PNmjVYs2YNhg0bhsWLF6Ndu3ZWrnXmxo0bh40bN2LkyJHYvn27tatDuRyDe3rp4uLi0KlTJzx69AjNmzfHrFmzEBQUpExPTk5GVFQUFi9eDCcnJyvWNPsmTZqESZMmWbsaisKFCyM2Ntba1ciSpUuXWrsKZEMKFCiAvXv36qX9/fff6NSpE86dO4ewsDCcO3cODg4Or6Q+tWrVeuXfqVWrVuH27dt477334OHhYTJfy5YtM+0SaI3656QBAwZgwYIFcHR0xCeffILBgwejaNGiynQRwZEjR/B///d/WLp0KY4ePZorgvvg4GBUqlQJO3bswJkzZ1CuXDlrV4lyMXbLoZdu8+bNuHPnDjw8PLBhwwa9wB541jLXokULrFq1CrVr17ZSLYkot6hUqRJmzpwJALh06RKOHDli5Rq9XPPmzQMA9OzZ08o1sa6ff/4ZCxYsgFqtxi+//ILJkyfrBfbAsy5wwcHBWLBgAY4cOYIKFSpYqbZZp9u///d//2flmlBux+CeXrpLly4BAMqUKQN3d/cszavr12yuD6wlD+tdvnwZPXv2RMGCBeHq6oqyZcti4sSJSE5ONjnP4cOH0bZtW+TJkwceHh6oUaMGfvzxR7PLMfdA7cqVK9G0aVP4+vrCyckJfn5+qFChAt577z389ddfeuuj6xqwZMkSvX6zGbuy6JY1fvx4PHjwAB9++CFKlCgBZ2dnpSuPuQdqMzp27Bjefvtt+Pv7w83NDVWrVsWcOXOg1WoN8ur6QoeHh5ss7/kHjnX10HXJmTBhgt56ZSwrswdqf/31V7Ro0QJ+fn5wcXFB0aJF0bNnT5w8edJoft0xFB4ejtTUVEyaNAnlypWDq6sr8uXLh169euH69etmt8/LkpiYiFWrVqFHjx4oX748vLy8oNFoULZsWQwfPhz//vuvwTy6ByIz+8v4nXmR5QD/29ehoaFIS0vDtGnTUKVKFbi7uxv04d6+fTsaN24MLy8veHt7o0GDBvjll19ycnPpydgQcPnyZYPpO3fuxIcffohq1arB399fOVa6d++Oo0ePGuQPDw9HiRIllP8/vz11x3JmD6TeuXMHI0aMQNmyZeHm5gYvLy/Url0bM2bMULqRZMWlS5dw8OBB5MmTJ0e6H5mrf8Z1PXLkCNq3bw8/Pz+4ubmhWrVqWLRokdmyr169iiFDhijd6nTrPmfOHKSlpWWr3lqtVjmm33vvPbRt2zbTeSpWrIjOnTvrpenORbrfjA0bNiA0NBR58+aFSqXC8ePHATx7Rmz27Nlo1aoVSpUqpezLWrVq4dtvvzX47UhNTUWBAgWgUqmwZ88ek3XasGEDVCoVypQpYzCtffv2AJ79VhBlB7vl0Evn5eUFADh//jz+++8/5M2b95Uu/8KFC/joo48QFxeHihUrwtPTE2fPnsVnn32GnTt3YuvWrXB1ddWbZ+PGjejYsSNSU1Ph6emJcuXK4datW+jfvz/++eefLNdh9OjR+OabbwAA+fPnR5UqVRAXF4crV67g9OnT0Gg0SrBSv359XLt2DdevXzfoQ1upUiWDsu/du4caNWrg+vXrKF++PMqXLw9HR8u/2vv378eXX34JtVqNcuXK4f79+zhx4gQGDRqEvXv3Yvny5dke6cTV1RX169fH+fPncffuXRQtWlSvz+/zd3NMGThwoNKqVahQIZQoUQLnz5/H8uXLsWbNGixZsgTdunUzOm9qaipatGiBXbt2ISgoCKVLl8bZs2fx888/IyYmBsePH3/lx+aRI0fQrVs3ODg4IH/+/ChdujQSExNx5coVfP/991i1ahX27t2LUqVKKfN4e3ujfv36Jss8ePAgUlNTs72cjEQEb731FjZv3owSJUqgfPnyuHPnjjJ97ty5+OCDDwAAvr6+KFGiBM6ePYuOHTti+vTp2dlEJiUkJCifjT2f0aJFC6Snp8PPzw8FCxZEkSJFcPXqVaxcuRLr1q3DmjVr9J5nCQoKQnBwMA4fPgwABtv4+XOEMSdOnMAbb7yBu3fvwsnJCRUrVkRCQgIOHjyIgwcPYs2aNdi6datyTrRETEwMAKBmzZqvbMShrVu3YtiwYXBzc0NgYCCuX7+O48ePo0+fPnjw4AE+/vhjg3k2bdqEd955B/Hx8cp8T58+xaFDh3Dw4EH89ttv2LhxI5ydnV+oTocOHcLFixcBAIMGDcrW+ulMmTIFo0aNgr+/v7KeOj/++CM+//xzuLq6omDBgqhUqRIePHiAo0eP4tChQ9iwYQN27dqlrI+TkxPCwsIwZcoU/PTTT2jYsKHRZf70008AgD59+hhMCwoKgo+PD+7evcuuOZQ9QvSSnT9/XtRqtQCQqlWryooVK+TevXsWzRsRESEAJCIiwmQeAGLsUA4JCREA4uTkJA0aNJBbt24p0/bt2ye+vr4CQMaMGaM33+3bt8XHx0cASN++fSUhIUFERLRarfz000/i4OAgjo6OAkAuX76sN29YWJgAkEWLFilp9+7dU+ZZt26daLVaZVp6erpERkbK77//bnS9w8LCTK63blkODg5Sq1YtuXLlijJNV+fLly8LAClevLjB/MWLF1e2T4cOHeTRo0fKtA0bNoirq6sAkPnz5+vNt2jRokzrpivb1PYxtz91+y0qKkovfcGCBQJAnJ2dZfny5Up6UlKSDBkyRACIq6urnD59Wm8+3bZ0cnKS0qVLy8mTJ5VpV69elXLlyhk9DjKjKzckJCTTvLpj9Pl1unLliqxatUoeP36sl/706VOl/DfeeMPiOv3www8CQPLkySNnz57N9nJ0+9rBwUH8/Pxk9+7dyjTdMfb333+Lk5OTAJDPP/9cUlNTRUQkLS1NvvzyS2VaVn9udMs2duyKiMyfP18AiFqtlqtXrxpMnzt3rly7dk0vLT09XdauXSvu7u6SJ08eiYuL05uu+76Yq2tUVJTR/Z6YmCilSpUSANKkSRO5ffu2Mu3QoUNSqFAhASC9e/fOZM319e3bN9PjU/e9MvedzKz+Iv87Tp2cnOTzzz+X5ORkZdrUqVMFgGg0GoPj6PTp06LRaMTBwUGmTJmiN9/x48elbNmyAkDGjh2b+QqbMG3aNOXYzo6M+9jZ2Vlmz54t6enpIvLs+NDVfc+ePbJjxw5JSUnRm//69evy1ltvCQD5+uuv9aadO3dOAIi7u7s8ffrUYNn//vuvODo6iqOjo/z7779G69e8eXMBIPPmzcvWetLrjcE9vRJTp04VlUqlnFQBSEBAgHTq1ElmzZold+/eNTpfTgT3zs7OcvPmTYPpuuDB09NT70Q8fvx4ASAlS5aUtLQ0g/kGDhyoLNOS4P7AgQPKhY2lshLcu7i4yPXr143msSS49/f3VwI1Y3UoWbKk3gWJNYJ7rVYrJUqUEAAyevRoo/MFBwcLAHn33XeNrgcAOXDggMF8v/zyiwCQKlWqmKyTMRnLtfTv+eA+M/Xr1xcAJgOBjLZv365cRO7cuTNHlqPb1wBk7dq1RucNDw83e5HTokWLHAvutVqtXL9+XebMmSNeXl4WB7TPGzt2rACQVatW6aVnJ7hfvHixABAPDw958OCBwXwbN25ULkae/16Y07RpUwEgs2bNMplH972y5NizJLhv0aKF0eVUqVJFAMiGDRv00rt06SIAZNy4cUbnO3bsmKhUKvHy8pLExESL1vt5w4cPFwBSrVq1F5pfJ+M+HjRo0AuVER8fL05OTlK2bFmDaaGhoQJAfvzxR4Np33zzjQCQdu3amSxbty9NneeILMFuOfRKfPzxx6hXrx6+/fZbbN26VekScOXKFaxbtw4jR47EuHHj8Omnn+b4sjt06IBChQoZpHfv3h0fffQRHj58iH379uHNN98E8OyWNAB88MEHRkfg+PDDD5UH3Cyh635y7tw5HDt2DNWqVXuR1TCpWbNmKFKkyAvP37dvX7i5uRmkDxo0CBMmTMClS5dw7tw5o31EX5WzZ88q/aqHDRtmNM9HH32E7t27448//jA6vUqVKqhTp45Bui5Nd8s/q7y8vIx2l8po3759JqeJCHbs2IEtW7bgwoULePLkifKsw/nz5wEAx48fR4sWLUyWcfbsWXTp0gXp6emYM2cOmjZtmqPL8fLywttvv2102brvi6khGj/88EOT+8QSV69eNdodRaPRYNiwYZgyZYrJef/++2+sXbsW//zzDx4+fKh0V7p79y6AZ8+adO3a9YXrlpFuO/Tq1cto9642bdqgTJkyOHv2LLZv34733nvPonLv3bsHABZ1GTM3FGZWhucdMGCA0fTatWvjxIkTet+VlJQUbNy4EQBMrlPVqlUREBCAy5cv4+jRo6hXr57FddHRjVlv7rktDw8PxMfH66X5+vri/v37RvObe24IePasyrp167B7925cv34d8fHxEBEAgFqtxtmzZ5GYmKh3/uzXrx+io6OxcOFC9O3bV6883TMLz6dnpNvPumOU6EUwuKdXpl69eqhXrx5SU1Nx7NgxHDlyBNu3b8cff/yBxMREjB49Gmq1Gp988kmOLrd8+fJG052dnREYGIhDhw7h7NmzSnCvGybO1HxBQUFwdHS0+AGxQoUKoXv37lixYgVq1KiBunXronHjxqhfvz4aNWqU5YeMn5fdfpmm1tPf3x/+/v64d+8ezp49a/XgXlenfPnyGc2jC7D//fdfPHnyxKBfs6mgJ3/+/ADwwi9Qq1atWqYvpzLVV/rp06d46623sGvXLrPzP3jwwOS0hw8fKu+QGDx4MN5///0cX05QUJDRC91Hjx4pfe9NHUem0i2VcZz75ORkXL58GQ8ePIBGo0FISIjJ4XNHjhyJb7/9VgnGjDG3XbNKd4xWrFjRZJ6KFSvi7NmzWRqKMikpCYBlff4tGQrTEln5rpw/fx6JiYlQq9VmL5R0AfaNGzcAPLuwMjdmv45uGFTdOP3PB+8Z1atXT3kW4/Hjx5k+H2Xu3Hnq1Cm0adMm0xel/ffff3rvEenYsSOGDBmCAwcOIDY2FmXLlgXw7AL/7NmzKFCgAFq1amWyPN2Fgm6/E70IjpZDr5yTkxNq1aqF999/Hxs2bMCZM2eU4cq++uqrFxpRwhxTwSDwvx+rjG8y1P1wmZrPwcEBvr6+WarDokWLMHHiRAQEBGD//v2YOHEiWrVqBX9/fwwcOBCPHz/OUnkZZffiIKvbxxp0y9fVx5gCBQoY5M/I1HZSq613GhwxYgR27dqFkiVLYuXKlbh69SqSkpIgz7pMolevXgBg8ICsTlpaGjp37ozz58/jjTfewPfff/9SlmNq22UM8kwdR+b2mSV049zv3bsXhw4dwu3btzFjxgw8ePAAnTt31htpSmfFihWYNm0aXFxc8N133+H06dOIi4uDVquFiGDhwoUATK/vi8jKMZqV75PuXPPw4cNs1C5rMvuuZLxgevToEYBno9ns27fP5J9unTMG3+by6/50dAG0uWB7+/btyrGiGyr1RdYzPT0dnTp1wpUrV9C0aVPs2LEDd+7cQUpKivKd0Q3B+fwx5OrqqnyfdA/PZvzcu3dvswMe/PfffwCQ5d8YoowY3JPVBQQEYPLkyQCe/eidPn1amaZr8TTV+mauFUfH3O1NXatjxte5614SY2q+9PT0LLf4OTs7Y8yYMbh06RIuXbqEpUuX6o1p3L179yyVl5Oyun0y2yeAZfslK3TLzzhCy/Nu375tkN+WpaWlKUPe/f7773jnnXdQrFgxuLi4KHkyO84+/PBDREZGomzZslizZo3R1vWcWI4pGV+oZOo4MrfPXoSjoyM+/PBDDBo0CGlpaejbty/S09P18uhehDZt2jQMGzYM5cqVg7u7u3Ls5mSLvU5WjtGsHJ+6iwVd0GdrdMeARqNRAl9zf7quMKGhoRbl19ENA/rw4UP8/fffL3WdDh06hNjYWBQtWhQbN25Es2bNkC9fPuUukYiYvdjq378/AGDZsmVIS0tDfHw81qxZA8D4KDkZ6Y5Nc40uRJlhcE82IeMQfBnHD9a1rJj6wTx37lymZWe8WMgoNTVV6TuascuJ7jaqqfnOnTuXrTGbS5QogV69emHZsmVKd44tW7boDcP2qoa8A0yv5/3795X+vhm3T2b75OHDhyb7uL7oeumWf+/ePZPL1d2CL1iwYJaGGrSWe/fuIS4uDnnz5jX6op20tDRlWEZjZs+ejblz5yJv3rzYuHGjyT7V2V2OOT4+Pkrwaeo4MpWeXRMmTICXlxdOnTqFZcuW6U3TPZ/RqFEjo/P++eefRtOz873THaPmuoLopunOMZbQPaPzsrZjdpUuXRrOzs5ISEgw+r6BnBIcHKz8TsyaNeulLQf43/FTs2ZNo88j/fPPP2a78VWsWBF16tTB7du3sXnzZqxduxZxcXFo0KBBpt0bdfu5evXq2VgDet0xuKeX7t69e2ZbeYH/PXCoVqv1An1d309TP8Zz5szJdPm//PKL0Zf0rFixAv/99x88PDz0xrTWPVA4d+5coy9xsuR2r6Vq1KihtKDeunVLSdf9oCQmJubYskz56aefjPbvnD17NoBnFyMZx6HX7ZNjx44ZfQmYuX3youtVpkwZ5QVDM2bMMJpHN556y5Yts1S2tejGZn/y5InemO06S5cuNdkavnPnTgwbNgxOTk5Yv349AgMDX8pyLKH7vuiOl+fl5Pclo7x58yrjnU+cOFGv9V63zsa+97GxscoDoM/LGMhl9RjVHXfLli0z2sq+ZcsWnD17Fmq1Gm+88YbF5YaEhAB49v4CW6TRaNC6dWsAwLfffvvSluPg4IBx48YBABYsWIBff/31pS3L3PEDAFOnTs20jH79+gF4dn7Vdckx9yAt8OzO9ZkzZ+Dg4GD2XRZEmWFwTy/d8uXLUblyZcydO9eg1TUlJQWLFy/GiBEjAABvvfUW/Pz8lOmNGzeGRqPBiRMn9E6o6enpmDlzJn7++edMly8i6Natm163jQMHDmDkyJEAgMGDB+t1Lxg4cCC8vb1x8eJFDBw4UO9HfsmSJViwYEGWXhIVGRmJESNGGLxBNTU1FV9//TWSk5Oh0Wj0Hu7SXeAcOnTIaECWkx4+fIjevXvjyZMnStrGjRuVrlKjRo3Sa9GsVKkSihUrhrt372LkyJF6QdXq1avx9ddfm3zIUbde+/fvz9LdD5VKhTFjxgB4FkCsWrVKmZacnIzhw4fj0KFDcHV1VfarrfP29kblypWRlpaGwYMH611grVu3DkOGDDH6EOW5c+fQpUsXpKWlYfbs2SbflJrd5VhqxIgRcHR0RFRUFCZMmKAcD+np6Zg0aRJ27tz5wmVn5qOPPoJGo8GFCxewYsUKJV3XYj9mzBi9AO3EiRNo27at0e5LwLMHtnVdZnQvj7JU165dUapUKcTFxaFr1656F0xHjx5VRqDp1atXpm+MzqhOnTrw8fHBmTNnXkp3opzw1Vdfwd3dHbNnz8Ynn3xi0GUlISEBGzZsULqrvKjevXvj3XffhYigU6dOGDFiBK5evWqQ79y5c0rXrBdRp04dODk54cCBA5g/f76SnpKSgs8//xw///xzpi/jeuedd+Dp6YnNmzdjz5498PT0NHhb7vP27t0LrVaLevXq5Yq7j2TDXu5Im0Qi33//vd54y0WLFpWaNWtKuXLlxN3dXUmvUaOG0fHup0+fruTx9/eX4OBgyZs3r6jValm4cGGm49yPHTtW8uTJI87OzlKtWjUpU6aMMk/Dhg2NjvG+YcMG5UVVXl5eUrNmTSlcuLAAkKFDh2Y6jnvGce43bNigLC9PnjxSvXp1qVatmvKiLJVKJQsWLNAr5/Hjx8pLtnx9faVu3boSEhIiQ4cONViWuTHjLRnnfuLEieLq6ioajUaCg4MlICBAqW+XLl30xrjXWbdunfLeAh8fHwkODpYCBQoo9TG1fS5evCguLi4CQAoWLCj169eXkJAQmTRpksF+MzYm/HvvvafUrUiRIlKzZk3x9vYWAOLo6Kj3cisdS94ZYOoYMicnXmK1detWcXBwEADi7e0tNWrUUI6z5s2bS8+ePQ2OJ91yXVxcpH79+ib/Fi5cmK3liPxvrPnM1nHmzJnKOvr5+UnNmjXF399fAOh9f7Mis5dY6ejGPy9TpozyMqIbN25Ivnz5lO1UuXJl5XtfuHBhmThxosljon///gI8e3FXtWrVJCQkREJCQpR3AJgbJ/748ePKcnXnG90LnABI3bp1DV4AZYkPP/xQAMjs2bONTs/pl1iZGoff3HtHtm/frpzTHB0dpUKFClK7dm0JCgpSjr3M9qUltFqtfP7553ovRytSpIgEBwdLzZo1pWjRokq6j4+PfPfdd3rzW/IuAxGRMWPGKPkKFSokwcHByrlmwoQJJs9xGWU8X/Xr1y/TddN9D5ctW2bJpiAyicE9vXQpKSkSHR0tY8aMkfr160u+fPnE0dFRXFxcpEiRItKmTRtZtGiR8mZLY5YtWyY1atQQV1dX8fb2lqZNmyov6sksuF+0aJFcunRJevToIQUKFBBnZ2cpXbq0fPHFF5KUlGRymX/99Ze0atVKvL29RaPRSPXq1ZW3tWYluL9//77MnDlT3nrrLSlVqpR4eHiIk5OTFC1aVN555x2jL1YSETly5Ii0adNG/Pz8lDf8ZvxBzqngPioqSo4ePSpvvfWW+Pr6iouLi1SqVElmzpypBEvGbNmyRRo0aCDu7u7i4eEhdevWlTVr1pjdPiIiO3fulCZNmoiPj49ygZAxKDEX3Is8e+lU8+bNJW/evOLk5CSFCxeWHj16yIkTJ4zmt+XgXkRk165d0rhxY3F3dxeNRiOVKlWSSZMmSUpKitHjydKXZz1/XGR1OSKWB/cizy4gQkJCxMPDQzw9PaV+/fqyfv16vfXPCkuD+1u3bikXjCtWrFDSL126JO+88474+vqKs7OzBAQEyAcffCB37twx+yK2uLg4+eijj6RUqVLi7OxsEPCaC45Fnr3h+qOPPpLSpUuLi4uLeHh4SM2aNeW7774ze74x59SpU8rFgTG2ENyLPFv3sWPHSrVq1cTT01McHR3Fz89PGjVqJJMmTdJ7a3J2Xb16VT777DOpW7eu+Pv7i5OTk3h6ekpgYKB06dJFFi9ebPQtsZYG9yIi8+bNk0qVKomzs7P4+PhIo0aNlJe5WRLc//XXX8qyTJ3ndeLi4sTDw0Py5s37wi/6ItJRiWTSGZqIiIisqkuXLli7di0OHDhg9GVsZHt+//13tG/fHuXLl8epU6fM5v3uu+/w0UcfYcqUKbmmayHZLgb3RERENu7SpUsoV64cQkJCsH37dmtXhyzQtm1bbNq0CdOnT8fw4cNN5ktISEDJkiXh5uaG2NhYvWFqiV4E31BLRERk40qWLIklS5bg7NmzePr0aa54l8PrLCYmBps3b4aHhwfCwsLM5r1y5QoGDhyIpk2bMrCnHMGWeyIiIqIcEBoaivj4eBw7dgzp6en48ssv8dlnn1m7WvSaYXBPRERElANUKhXUajWKFi2Kfv36YcyYMVCrOeo4vVrslkNERESUA9heSraAl5NERERERHaCwT0RERERkZ1gcE9EREREZCcY3BMRERER2QkG90REREREdoLBPRERERGRnWBwT0RERERkJxjcExERERHZCQb3RERERER2gsE9EREREZGdYHBPRERERGQnGNwTEREREdkJBvdERERERHaCwT0RERERkZ34f18OVuSvhFIYAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 780x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Figure 2: Fine–Gray new-user + lag (30d vs 90d)\n",
    "rows_lag = [\n",
    "    {\"label\": \"New users (lag 90 d)\", \"hr\": 0.712, \"lcl\": 0.556, \"ucl\": 0.911},\n",
    "    {\"label\": \"New users (lag 30 d)\", \"hr\": 0.799, \"lcl\": 0.628, \"ucl\": 1.020},\n",
    "]\n",
    "\n",
    "df2 = pd.DataFrame(rows_lag)\n",
    "df2['y'] = np.arange(len(df2))[::-1]\n",
    "\n",
    "fig2, ax2 = plt.subplots(figsize=(5.2, 3.2))\n",
    "xerr2 = np.vstack([df2[\"hr\"] - df2[\"lcl\"], df2[\"ucl\"] - df2[\"hr\"]])\n",
    "ax2.errorbar(df2[\"hr\"], df2[\"y\"], xerr=xerr2, fmt='o', capsize=3, color='black', lw=1.2, ms=4)\n",
    "ax2.axvline(1.0, linestyle='--', linewidth=1, color='black')\n",
    "\n",
    "ax2.set_xscale('log')\n",
    "ax2.set_yticks(df2[\"y\"])\n",
    "ax2.set_yticklabels(df2[\"label\"])\n",
    "ax2.set_xlabel(\"Subdistribution Hazard Ratio (Fine–Gray)\")\n",
    "\n",
    "xmin2 = max(0.5, df2[\"lcl\"].min() * 0.9)\n",
    "xmax2 = max(1.5, df2[\"ucl\"].max() * 1.12)\n",
    "ax2.set_xlim(xmin2, xmax2)\n",
    "ticks2 = [0.5, 0.75, 1.0, 1.25, 1.5]\n",
    "ax2.set_xticks([t for t in ticks2 if xmin2 <= t <= xmax2])\n",
    "ax2.xaxis.set_major_formatter(ScalarFormatter())\n",
    "ax2.grid(False)\n",
    "\n",
    "for _, r in df2.iterrows():\n",
    "    txt = f\"{r.hr:.3f} [{r.lcl:.3f}; {r.ucl:.3f}]\"\n",
    "    ax2.text(xmax2*1.02, r.y, txt, va='center', fontsize=9, color='black', clip_on=False)\n",
    "\n",
    "ax2.invert_yaxis()\n",
    "fig2.tight_layout()\n",
    "png2 = \"/mnt/data/fig_finegray_newuser_lag_forest_bw.png\"\n",
    "pdf2 = \"/mnt/data/fig_finegray_newuser_lag_forest_bw.pdf\"\n",
    "#fig2.savefig(png2, dpi=300, bbox_inches=\"tight\")\n",
    "\n",
    "(png1, pdf1, png2, pdf2)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "9a5756d5-6432-4c27-a08d-c2032ec11f82",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1050x420 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# --- Data (Fine–Gray) ---\n",
    "data = [\n",
    "    (\"New users (lag 30 d)\", 0.799, 0.628, 1.020, 0.069),\n",
    "    (\"New users (lag 90 d)\", 0.712, 0.556, 0.911, 0.0071),\n",
    "]\n",
    "df = pd.DataFrame(data, columns=[\"Model\",\"HR\",\"LCL\",\"UCL\",\"p\"])\n",
    "\n",
    "def fmt_p(p): return \"< 0.001\" if float(p) < 0.001 else f\"{float(p):.3f}\"\n",
    "df[\"p_str\"] = df[\"p\"].apply(fmt_p)\n",
    "\n",
    "df[\"logHR\"]  = np.log(df[\"HR\"])\n",
    "df[\"logLCL\"] = np.log(df[\"LCL\"])\n",
    "df[\"logUCL\"] = np.log(df[\"UCL\"])\n",
    "\n",
    "ROW_GAP = 1.2\n",
    "df[\"y\"] = np.arange(len(df))[::-1] * ROW_GAP\n",
    "\n",
    "# === Estilo preto ===\n",
    "COLOR = \"black\"\n",
    "plt.rcParams.update({\n",
    "    \"text.color\": COLOR,\n",
    "    \"axes.labelcolor\": COLOR,\n",
    "    \"axes.edgecolor\": COLOR,\n",
    "    \"xtick.color\": COLOR,\n",
    "    \"ytick.color\": COLOR,\n",
    "})\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(7.0, 2.8), constrained_layout=False)\n",
    "\n",
    "hr_ticks  = np.array([0.5, 0.75, 1.0, 1.25, 1.5])\n",
    "log_ticks = np.log(hr_ticks)\n",
    "\n",
    "xmin = min(df[\"logLCL\"].min(), log_ticks.min())\n",
    "xmax = max(df[\"logUCL\"].max(), log_ticks.max())\n",
    "pad  = 0.10 * (xmax - xmin)\n",
    "ax.set_xlim(xmin - pad, xmax + pad)\n",
    "ax.set_ylim(-0.6*ROW_GAP, (len(df)-1)*ROW_GAP + 0.6*ROW_GAP)\n",
    "ax.set_yticks([])\n",
    "\n",
    "# CIs (pretos)\n",
    "for _, r in df.iterrows():\n",
    "    ax.hlines(r[\"y\"], r[\"logLCL\"], r[\"logUCL\"], linewidth=1.8, color=COLOR)\n",
    "\n",
    "# Pontos (pretos)\n",
    "ax.plot(df[\"logHR\"], df[\"y\"], marker=\"s\", linestyle=\"None\",\n",
    "        markersize=6, markerfacecolor=COLOR, markeredgecolor=COLOR, color=COLOR)\n",
    "\n",
    "# Linha HR=1 (preta)\n",
    "ax.axvline(0.0, linestyle=\"--\", linewidth=1.0, color=COLOR)\n",
    "\n",
    "# Ticks e rótulos (pretos)\n",
    "in_range = (log_ticks >= ax.get_xlim()[0]) & (log_ticks <= ax.get_xlim()[1])\n",
    "ax.set_xticks(log_ticks[in_range])\n",
    "ax.set_xticklabels([f\"{t:.2g}\" for t in hr_ticks[in_range]])\n",
    "ax.set_xlabel(\"Subdistribution HR (Fine–Gray)\")\n",
    "\n",
    "# Estética (spines pretas onde visíveis)\n",
    "for s in [\"left\",\"right\",\"top\"]:\n",
    "    ax.spines[s].set_visible(False)\n",
    "ax.spines[\"bottom\"].set_color(COLOR)\n",
    "ax.grid(False)\n",
    "\n",
    "ax.set_position([0.30, 0.30, 0.52, 0.62])\n",
    "\n",
    "trans = ax.get_yaxis_transform()\n",
    "X_MODEL = -1.30\n",
    "X_HR    = -0.83\n",
    "X_P     =  1.15\n",
    "\n",
    "# Cabeçalhos (pretos)\n",
    "ax.text(0.50, 1.06, \"Statins protective   |   Statins harmful\",\n",
    "        ha=\"center\", va=\"bottom\", transform=ax.transAxes, fontsize=10, color=COLOR)\n",
    "ax.text(X_MODEL, (len(df)-1 + 0.5)*ROW_GAP, \"Model\",\n",
    "        ha=\"left\", va=\"bottom\", transform=trans, fontsize=10, fontweight=\"bold\", color=COLOR)\n",
    "ax.text(X_HR,   (len(df)-1 + 0.5)*ROW_GAP, \"Subdistribution Hazard Ratio (95% CI)\",\n",
    "        ha=\"left\", va=\"bottom\", transform=trans, fontsize=10, fontweight=\"bold\", color=COLOR)\n",
    "ax.text(X_P,     (len(df)-1 + 0.5)*ROW_GAP, \"p-value\",\n",
    "        ha=\"right\", va=\"bottom\", transform=trans, fontsize=10, fontweight=\"bold\", color=COLOR)\n",
    "\n",
    "# Linhas de texto (pretas)\n",
    "for _, r in df.iterrows():\n",
    "    y = r[\"y\"]\n",
    "    ax.text(X_MODEL, y, r[\"Model\"], ha=\"left\",  va=\"center\", transform=trans, fontsize=9, color=COLOR)\n",
    "    ax.text(X_HR,    y, f\"{r.HR:.3f} ({r.LCL:.3f}–{r.UCL:.3f})\",\n",
    "            ha=\"left\",  va=\"center\", transform=trans, fontsize=9, color=COLOR)\n",
    "    ax.text(X_P,     y, r[\"p_str\"], ha=\"right\", va=\"center\", transform=trans, fontsize=9, color=COLOR)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "60a39dd8-a369-49f8-82fe-fe0d9e862b0e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Model</th>\n",
       "      <th>HR</th>\n",
       "      <th>LCL</th>\n",
       "      <th>UCL</th>\n",
       "      <th>p</th>\n",
       "      <th>p_str</th>\n",
       "      <th>logHR</th>\n",
       "      <th>logLCL</th>\n",
       "      <th>logUCL</th>\n",
       "      <th>y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>New users (lag 30 d)</td>\n",
       "      <td>0.799</td>\n",
       "      <td>0.628</td>\n",
       "      <td>1.020</td>\n",
       "      <td>0.0690</td>\n",
       "      <td>0.069</td>\n",
       "      <td>-0.224394</td>\n",
       "      <td>-0.465215</td>\n",
       "      <td>0.019803</td>\n",
       "      <td>1.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>New users (lag 90 d)</td>\n",
       "      <td>0.712</td>\n",
       "      <td>0.556</td>\n",
       "      <td>0.911</td>\n",
       "      <td>0.0071</td>\n",
       "      <td>0.007</td>\n",
       "      <td>-0.339677</td>\n",
       "      <td>-0.586987</td>\n",
       "      <td>-0.093212</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  Model     HR    LCL    UCL       p  p_str     logHR  \\\n",
       "0  New users (lag 30 d)  0.799  0.628  1.020  0.0690  0.069 -0.224394   \n",
       "1  New users (lag 90 d)  0.712  0.556  0.911  0.0071  0.007 -0.339677   \n",
       "\n",
       "     logLCL    logUCL    y  \n",
       "0 -0.465215  0.019803  1.2  \n",
       "1 -0.586987 -0.093212  0.0  "
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0a922e35-da2b-4865-b6c8-dbbd52027908",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "172a31ab-3c4e-4df9-a5ac-7d4469889d89",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>variable</th>\n",
       "      <th>N</th>\n",
       "      <th>observed</th>\n",
       "      <th>missing</th>\n",
       "      <th>observed_%</th>\n",
       "      <th>median_days [Q1;Q3]</th>\n",
       "      <th>min_days</th>\n",
       "      <th>max_days</th>\n",
       "      <th>≤0 n (%) among observed</th>\n",
       "      <th>=0 n (%) among observed</th>\n",
       "      <th>&gt;0 n (%) among observed</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>bmi_days</td>\n",
       "      <td>38806</td>\n",
       "      <td>32390</td>\n",
       "      <td>6416</td>\n",
       "      <td>83.5</td>\n",
       "      <td>0.1 [0.0; 87.9]</td>\n",
       "      <td>-2943.042361</td>\n",
       "      <td>3670.104861</td>\n",
       "      <td>4919 (15.2%)</td>\n",
       "      <td>15 (0.0%)</td>\n",
       "      <td>27471 (84.8%)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>creatinine_first_days</td>\n",
       "      <td>38806</td>\n",
       "      <td>30643</td>\n",
       "      <td>8163</td>\n",
       "      <td>79.0</td>\n",
       "      <td>0.7 [-0.2; 89.7]</td>\n",
       "      <td>-2971.213889</td>\n",
       "      <td>3297.394444</td>\n",
       "      <td>8271 (27.0%)</td>\n",
       "      <td>5 (0.0%)</td>\n",
       "      <td>22372 (73.0%)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>hb_glic_days</td>\n",
       "      <td>38806</td>\n",
       "      <td>22162</td>\n",
       "      <td>16644</td>\n",
       "      <td>57.1</td>\n",
       "      <td>7.9 [-34.0; 240.7]</td>\n",
       "      <td>-2945.538194</td>\n",
       "      <td>3295.972917</td>\n",
       "      <td>8127 (36.7%)</td>\n",
       "      <td>0 (0.0%)</td>\n",
       "      <td>14035 (63.3%)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                variable      N  observed  missing  observed_%  \\\n",
       "0               bmi_days  38806     32390     6416        83.5   \n",
       "1  creatinine_first_days  38806     30643     8163        79.0   \n",
       "2           hb_glic_days  38806     22162    16644        57.1   \n",
       "\n",
       "  median_days [Q1;Q3]     min_days     max_days ≤0 n (%) among observed  \\\n",
       "0     0.1 [0.0; 87.9] -2943.042361  3670.104861            4919 (15.2%)   \n",
       "1    0.7 [-0.2; 89.7] -2971.213889  3297.394444            8271 (27.0%)   \n",
       "2  7.9 [-34.0; 240.7] -2945.538194  3295.972917            8127 (36.7%)   \n",
       "\n",
       "  =0 n (%) among observed >0 n (%) among observed  \n",
       "0               15 (0.0%)           27471 (84.8%)  \n",
       "1                5 (0.0%)           22372 (73.0%)  \n",
       "2                0 (0.0%)           14035 (63.3%)  "
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('tbl_clean_v1.csv').iloc[:,1:]\n",
    "\n",
    "# Age -> Int64\n",
    "if \"age\" in df.columns:\n",
    "    df[\"age\"] = pd.to_numeric(df[\"age\"], errors=\"coerce\")\n",
    "    df[\"age\"] = df[\"age\"].round().astype(\"Int64\")\n",
    "\n",
    "# Gender -> male (1/0)\n",
    "if \"male\" not in df.columns and \"gender\" in df.columns:\n",
    "    df[\"male\"] = np.where(\n",
    "        df[\"gender\"].astype(str).str.strip().str.upper().eq(\"MALE\"),\n",
    "        1, 0\n",
    "    ).astype(\"int8\")\n",
    "\n",
    "# Insert index day\n",
    "df['index_day'] = 0\n",
    "\n",
    "# --- configure variables of interest ---\n",
    "days_cols = ['bmi_days', 'hb_glic_days', 'creatinine_first_days']\n",
    "\n",
    "def _median_iqr(x: pd.Series):\n",
    "    x = pd.to_numeric(x, errors='coerce').dropna()\n",
    "    if x.empty:\n",
    "        return np.nan, np.nan, np.nan\n",
    "    med = float(np.median(x))\n",
    "    q1, q3 = np.percentile(x, [25, 75])\n",
    "    return med, float(q1), float(q3)\n",
    "\n",
    "def summarize_days_vars(df: pd.DataFrame, cols: list[str]) -> pd.DataFrame:\n",
    "    rows = []\n",
    "    N = len(df)\n",
    "    for c in cols:\n",
    "        if c not in df.columns:\n",
    "            rows.append({\n",
    "                \"variable\": c, \"N\": N, \"observed\": 0, \"missing\": N, \"observed_%\": 0.0,\n",
    "                \"median_days [Q1;Q3]\": \"NA\", \"min_days\": \"NA\", \"max_days\": \"NA\",\n",
    "                \"≤0 n (%) among observed\": \"NA\", \"=0 n (%) among observed\": \"NA\",\n",
    "                \">0 n (%) among observed\": \"NA\"\n",
    "            })\n",
    "            continue\n",
    "\n",
    "        x = pd.to_numeric(df[c], errors='coerce')\n",
    "        obs = int(x.notna().sum())\n",
    "        miss = int(N - obs)\n",
    "        obs_pct = round(100 * obs / N, 1) if N else np.nan\n",
    "\n",
    "        med, q1, q3 = _median_iqr(x)\n",
    "        med_iqr_str = \"NA\" if np.isnan(med) else f\"{med:.1f} [{q1:.1f}; {q3:.1f}]\"\n",
    "\n",
    "        # distribuição relativa ao índice (dia 0), entre observados\n",
    "        n_le0 = int((x <= 0).sum(skipna=True)) if obs else 0\n",
    "        n_eq0 = int((x == 0).sum(skipna=True)) if obs else 0\n",
    "        n_gt0 = int((x > 0).sum(skipna=True))  if obs else 0\n",
    "        p = lambda n: (100 * n / obs) if obs else np.nan\n",
    "\n",
    "        row = {\n",
    "            \"variable\": c,\n",
    "            \"N\": N,\n",
    "            \"observed\": obs,\n",
    "            \"missing\": miss,\n",
    "            \"observed_%\": obs_pct,\n",
    "            \"median_days [Q1;Q3]\": med_iqr_str,\n",
    "            \"min_days\": \"NA\" if obs==0 else float(np.nanmin(x)),\n",
    "            \"max_days\": \"NA\" if obs==0 else float(np.nanmax(x)),\n",
    "            \"≤0 n (%) among observed\": \"NA\" if obs==0 else f\"{n_le0} ({p(n_le0):.1f}%)\",\n",
    "            \"=0 n (%) among observed\":  \"NA\" if obs==0 else f\"{n_eq0} ({p(n_eq0):.1f}%)\",\n",
    "            \">0 n (%) among observed\":  \"NA\" if obs==0 else f\"{n_gt0} ({p(n_gt0):.1f}%)\",\n",
    "        }\n",
    "        rows.append(row)\n",
    "\n",
    "    out = pd.DataFrame(rows)\n",
    "    # Ordenar por % observado (opcional)\n",
    "    out = out.sort_values(\"observed_%\", ascending=False).reset_index(drop=True)\n",
    "    return out\n",
    "\n",
    "summary_days = summarize_days_vars(df, days_cols)\n",
    "summary_days\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "a090765d-b93f-4a02-86de-dee15f0bf9f0",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df[['patient_id', 'age', 'male', 'bmi', 'hb_glic',\n",
    "        'creatinine_first', \n",
    "        'microalb', 'dm_nm', 'dm_days',\n",
    "       'ckd_non_dialysis', 'ace_arb_days', 'statin_nm',\n",
    "       'statin_days', 'hypertension_days', 'cvd_days', 'last_visit',\n",
    "       'dialysis_days', 'death_days', 'index_day']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "01aba412-cfc9-4cd7-bab5-f3eb32f7f2f6",
   "metadata": {},
   "outputs": [],
   "source": [
    "# -------------------\n",
    "# DM1 / DM2 by ICD\n",
    "# -------------------\n",
    "if \"dm_nm\" in df.columns:\n",
    "    codes = (\n",
    "        df[\"dm_nm\"]\n",
    "        .astype(str)\n",
    "        .str.extract(r\"\\[([A-Z]\\d{2}(?:\\.\\d)?)\\]\", expand=False)  # pega algo tipo E10, E10.9, O24.0\n",
    "        .str.upper()\n",
    "    )\n",
    "    dm1_mask = codes.str.startswith(\"E10\") | codes.eq(\"O24.0\")\n",
    "else:\n",
    "    dm1_mask = pd.Series(False, index=df.index)\n",
    "\n",
    "df[\"dm1\"] = dm1_mask.fillna(False).astype(\"int8\")\n",
    "df[\"dm2\"] = (1 - df[\"dm1\"]).astype(\"int8\")  # sua regra: se não DM1, vira DM2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "45544024-f2bd-4161-a77a-1eb61f39e6d2",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df[['patient_id', 'age', 'male', 'bmi', 'hb_glic',\n",
    "        'creatinine_first', \n",
    "        'dm2', \n",
    "       'ckd_non_dialysis', 'ace_arb_days', \n",
    "       'statin_days', 'hypertension_days','cvd_days', 'last_visit',\n",
    "       'dialysis_days', 'death_days', 'index_day']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "dd6e8f3d-7203-4e71-9d35-2478a75c2004",
   "metadata": {},
   "outputs": [],
   "source": [
    "# get just dm2\n",
    "df = df[df.dm2 == 1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "abc2a5ff-1acf-41f4-89d8-c6c92fcfe15b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "drop 232 patients without follow-up (no dialysis, death or last visit).\n",
      "Warning: 142 lines with end_time < 0 will be dropped.\n",
      "=== RESUMO INICIAL ===\n",
      "Pacientes únicos: 36246\n",
      "Eventos de diálise: 656\n",
      "Follow-up (mediana entre censurados), dias: 753.1\n",
      "\n",
      "Statin — categorias:\n",
      "  never         :  25474  | eventos: 237\n",
      "  start(>0)     :   8768  | eventos: 375\n",
      "  baseline(≤0)  :   2004  | eventos: 44\n",
      "\n",
      "Missing in principle covariables:\n",
      "hb_glic             15503\n",
      "creatinine_first     7782\n",
      "bmi                  5680\n",
      "age                     0\n",
      "male                    0\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "days_cols = [\n",
    "    'ckd_non_dialysis','ace_bra_days','statin_days','hypertension_days',\n",
    "    'cvd_days','last_visit','dialysis_days','death_days','index_day'\n",
    "]\n",
    "num_cols  = ['age','male','bmi','hb_glic','creatinine_first']\n",
    "\n",
    "for c in days_cols + num_cols:\n",
    "    if c in df.columns:\n",
    "        df[c] = pd.to_numeric(df[c], errors='coerce')\n",
    "\n",
    "assert 'index_day' in df.columns, \"Faltou index_day\"\n",
    "\n",
    "if not (df['index_day'].fillna(0)==0).all():\n",
    "    print(\"Aviso: há linhas com index_day != 0.\")\n",
    "\n",
    "# ==== 2) Define follow-up, event e censor ====\n",
    "# Censor = less them last visit and death (death = censor)\n",
    "df['censor_time'] = df[['last_visit','death_days']].min(axis=1, skipna=True)\n",
    "\n",
    "# Dialysis event occur if:\n",
    "# - dialysis date present AND (no censor before OR dialysis <= censor)\n",
    "df['event_dialysis'] = df['dialysis_days'].notna() & (\n",
    "    df['censor_time'].isna() | (df['dialysis_days'] <= df['censor_time'])\n",
    ")\n",
    "\n",
    "# Final follow up = event day (if occur) or time of censor\n",
    "df['end_time'] = np.where(df['event_dialysis'], df['dialysis_days'], df['censor_time'])\n",
    "\n",
    "# Drop missing follow-up \n",
    "mask_follow = df['end_time'].notna()\n",
    "n_drop_nofup = (~mask_follow).sum()\n",
    "df = df.loc[mask_follow].copy()\n",
    "if n_drop_nofup > 0:\n",
    "    print(f\"drop {n_drop_nofup} patients without follow-up (no dialysis, death or last visit).\")\n",
    "\n",
    "# Optional: drop negative time /impossible\n",
    "neg_end = (df['end_time'] < 0).sum()\n",
    "if neg_end > 0:\n",
    "    print(f\"Warning: {neg_end} lines with end_time < 0 will be dropped.\")\n",
    "    df = df.loc[df['end_time'] >= 0].copy()\n",
    "\n",
    "# ==== 3) Flags of baseline (occur until index_day) ====\n",
    "def baseline_flag(x):\n",
    "    return np.where(np.isfinite(x) & (x <= 0), 1, 0)\n",
    "\n",
    "df['hypertension_baseline']  = baseline_flag(df['hypertension_days'])\n",
    "df['cvd_baseline']  = baseline_flag(df['cvd_days'])\n",
    "df['ckd_baseline']  = baseline_flag(df['ckd_non_dialysis'])\n",
    "df['ace_arb_base'] = baseline_flag(df['ace_arb_days'])\n",
    "\n",
    "# ==== 4) Resumo rápido da coorte ====\n",
    "n_patients = df['patient_id'].nunique()\n",
    "n_events   = int(df['event_dialysis'].sum())\n",
    "follow_cens = df.loc[~df['event_dialysis'], 'end_time']\n",
    "median_fu_cens = float(np.nanmedian(follow_cens)) if not follow_cens.empty else np.nan\n",
    "\n",
    "# categorias de estatina\n",
    "statin_cat = pd.Series(np.select(\n",
    "    [\n",
    "        df['statin_days'].isna(),\n",
    "        df['statin_days'] <= 0,\n",
    "        df['statin_days'] > 0\n",
    "    ],\n",
    "    ['never', 'baseline(≤0)', 'start(>0)'],\n",
    "    default='unknown'\n",
    "), index=df.index)\n",
    "\n",
    "cat_counts = statin_cat.value_counts(dropna=False)\n",
    "cat_events = df.groupby(statin_cat)['event_dialysis'].sum().reindex(cat_counts.index).fillna(0).astype(int)\n",
    "\n",
    "print(\"=== RESUMO INICIAL ===\")\n",
    "print(f\"Pacientes únicos: {n_patients}\")\n",
    "print(f\"Eventos de diálise: {n_events}\")\n",
    "print(f\"Follow-up (mediana entre censurados), dias: {median_fu_cens:.1f}\")\n",
    "print(\"\\nStatin — categorias:\")\n",
    "for k, v in cat_counts.items():\n",
    "    print(f\"  {k:14s}: {int(v):6d}  | eventos: {int(cat_events.loc[k])}\")\n",
    "\n",
    "print(\"\\nMissing in principle covariables:\")\n",
    "miss = df[['age','male','bmi','hb_glic','creatinine_first']].isna().sum()\n",
    "print(miss.sort_values(ascending=False))\n",
    "\n",
    "# df agora está pronto para construir o dataset time-varying (PASSO 2)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0ad41752-0986-4be7-a42f-9fe4847d5e2f",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "9ac7e3b2-ccbf-40b1-8e84-bd1c310816e8",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.11/site-packages/lifelines/fitters/aalen_johansen_fitter.py:112: Warning: Tied event times were detected. The Aalen-Johansen estimator cannot handle tied event times.\n",
      "                To resolve ties, data is randomly jittered.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "image/png": 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fvkRCQkKxxpXJZJg4cSLS0tLkbVevXsXq1ashkUjwxRdfKOW4YcMGhXzCwsLkxeX7ivK9nT17NqRSKUaMGIHAwECle1JSUrBx40akp6cX7U2WgJGREaZPn47k5GT0799f5Qx1TEwMtm7dqrWciIiIqGLhMl2qcKZMmYJff/0VSUlJKq8vXrwYwcHBOHHiBOrVq4f27dsjNTUVp06dQkZGBiZNmoTevXurNdb8+fMxdepUNG7cGA0aNIAgCLhx4wYePHiAypUrY+rUqfLYli1bokqVKti9ezc6dOiAevXqQSqVYuTIkWjdujWmT5+Oo0ePYubMmfjnn3/g5OSE8PBwXLlyBVOnTsWKFSuUxu/atSuMjY2xatUq3L59GzVq1IBEIsG0adO0tiFNs2bN0KVLF5w4cQJOTk7o0KEDACAwMBC2trbo27evyiNp+vTpg19//RVNmzZF69atYWxsDGdnZ0ybNk2tcX18fHDjxg3Ur18f7dq1Q2JiIk6dOoXs7Gx8++23aNasmTx2xIgR+PHHH3HgwAE4OzujefPmiIuLw5kzZ9CvXz9cunRJadlvUb63bdu2xW+//YYJEyagY8eOcHd3h5OTEwwMDPD48WNcv34dmZmZ6N+/P0xMTIrxVS6emTNn4t69e9i6dSsaNmyIJk2awMHBAVlZWbh//z7CwsLg4eGBYcOGaS0nIiIiqjg4M0oVjrW1Nb766qt8r1tYWODMmTOYP38+bG1tsX//fgQFBaFZs2bYvn272meMAsCvv/6KgQMHIi0tDUeOHMHRo0ehr6+Pr7/+Gjdv3kSDBg3kscbGxjh06BC6dOmC69evY/PmzdiwYQMePHgAAGjXrh3OnTuHTp064eHDhzh48CAMDQ2xZ88ejB8/XuX4NWrUwL59+9CyZUucO3cOGzduxIYNG4p8LEtJ7du3D7Nnz4adnR2OHDmCq1evYuDAgQgODkalSpVU3rN48WJMmDABOTk52LVrFzZs2IBDhw6pPaaNjQ2Cg4PRuXNnnD59GoGBgXB1dcWmTZuwcOFCpdjLly9j8ODByMrKwv79+xETE4OFCxfmu3txUb63ADB27FhcuXIFfn5+SE5OxsGDB3Hs2DGkpKRgyJAhOHjwoNI5p5omlUrh7++Pffv2oUuXLnj06BH27NmDc+fOwdjYGNOmTcPGjRu1mhMRERFVHBJBl1tYUr7yNqi5c+eOjjMhIiIiorIkOj4NbZeeVmi7M78bzIy4KJJKF86MEhERERERkdaxGCUiIiIiIiKtYzFKREREREREWsdilIiIiIiIiLSOxSgRERERERFpHYtRIiIiIiIi0joWo0RERERERKR1LEaJiIiIiIhI61iMEhERERERkdaxGCUiIiIiIiKtYzFKREREREREWsdilIiIiIiIiLROq8VoTEwMLl++jJSUFG0OS0RERERERKWMqMXopUuX8PXXX+PQoUMK7UlJSejduzfq1KmDli1bonr16ti0aZOYQxMpOX36ND7++GPUrFkThoaGsLa2hrOzMz755BOsXr0aiYmJuk4RHTp0gEQiwePHj3WdimjKy3uqW7cuJBKJUrtEIkHdunW1n5CaEhISYGNjg08++URjY+Tm5mLVqlVo1KgRTExMYGdnh08//RR3794VdZx58+ZBIpFg8+bN8jZBENCkSRM0atQIMplM1PGIiIhIu0QtRjds2ICff/4ZZmZmCu0zZszAoUOHIAgCDA0NkZqaijFjxuDSpUtiDk8kt2DBAnTq1An//vsvrKys4OPjg65du8LExAT//vsvJk6cqPSLc37FR0mU9sIlP6qKACobfvjhB8THx2Pu3Lka6V8mk+GTTz7B119/jejoaPTq1Qtubm7YvXs3mjVrpvG/1yUSCb777jvcvn2bP59ERERlnKjF6Pnz52FqaooOHTrI29LS0uDv7w9TU1NcunQJqampmD9/PmQyGX755RcxhycCAFy9ehXz5s2DgYEB9u7di7CwMPz777/YtWsXrl+/jpiYGCxfvhyVKlXSdarw9/fH3bt3UbNmTV2nQmq6e/cuAgICdJ2GSs+fP8evv/6K3r17w93dXSNjbNy4EXv37kWDBg1w79497N69G4GBgfjnn3+QlpaGIUOGICcnRyNj5+nXrx9cXFzw3XffaXwsIiIi0hxRi9GXL1+idu3aCm1nz55Feno6Pv74YzRr1gxSqRTffPMNKlWqhHPnzok5PBEA4N9//4UgCPj000/Rr18/pevVqlXD1KlT4eLiov3k3lOnTh24uLjAwMBA16mQmlxcXFC/fn1dp6HSxo0bkZmZCV9f3xL3JQiCymW3K1euBAAsW7YMVatWlbd//PHH6NOnDyIiIrBv374Sj18QiUSCIUOGICYmBvv379foWERERKQ5ohajiYmJMDc3V2g7f/48JBIJunbtKm/T19eHg4MDXrx4IebwRACA2NhYAICdnZ1a8YGBgZBIJIiKigLw9hfdvD/vLrGNiIjAvHnz0KpVK1SrVg2GhoaoVasWfH198eDBA4U+N2/eLF/yGxUVpdDnuysH8nu+Mm/s3NxcLF26FE5OTjAyMkLt2rUxY8YMZGZmqv31EAQBO3bswMCBA+Hk5AQzMzNYWFigRYsWWLNmjdJzd3Xr1sX8+fMBACNGjFDIPTAwUO1xAWDbtm3w9PSEqakpqlSpAj8/P8TExCjFJSQk4Ndff0W3bt1gb28PIyMj2NjYoHv37jhx4oTKvlNSUrB48WI0btwYVlZWMDc3R/369fHJJ5/g2LFjSvFpaWlYvHgxmjRpAnNzc5ibm6Nly5bYsmVLkd6TqqXXeT9Dw4cPR1xcHL744gtUr14dRkZGcHd3x8aNG/Pt7+nTp5gwYQLq168PY2NjVK5cGT4+Prhw4UKR8hIEARs2bICFhQV69epVpHvzJCQk4O+//8bw4cNRvXp19OjRQ+H6o0ePcPfuXZiYmKgcY8CAAQCAAwcOFGnc/fv3o1WrVjA1NYWNjQ0+/vhjpf+m3jd48GAAwPr164s0FhEREZUe+mJ2ZmlpiejoaIW206dPAwDatm2r0C6RSGBkZCTm8PQumQxIj9N1FsVjUhmQFv9zkrzZ+T179uCbb75BlSpVCoyvVq0a/Pz8sHv3bqSmpsLPz09+zdbWVv7///zzTyxbtgzu7u5o3rw5jIyMEBYWhq1bt2Lfvn0ICgqCh4cHAMDR0RF+fn7YsmULzMzM5L+kAyjSjOzgwYNx+PBhdOjQAc7OzggKCsKyZcsQExODbdu2qdVHZmYmBg8eDBsbG7i6uqJp06Z48+YNLly4gPHjx+PSpUsKz94NGDAAJ0+exI0bN9CmTRs4OjoqfK3UtWLFCqxZswZeXl7o27cvgoOD4e/vj1OnTuHixYuoVauWPDY4OBiTJk1C3bp14ezsjFatWuHJkyc4fvw4jh8/jj///BMjR46Ux+fm5qJz584ICQmBra0tOnToAGNjY0RHR+Pw4cMwMzNDt27d5PGvXr1Cly5dcPPmTVSrVg3t27eHIAi4cOEChg8fjitXruDXX39V+73lJyEhAa1atUJKSgq8vLzw+vVrnD17FqNGjYJMJsPo0aMV4i9evIhevXohPj4ezs7O6NWrF2JjY3Hs2DEcPXoUf/31Fz777DO1xg4LC8OjR4/QpUsXGBsbq53zjRs3cPjwYRw+fBgXL15Ebm4ugLez9gMHDlSKBQB3d3eVs/lNmzYFANy8eVPt8detW4cvvvgCEokEXl5eqF69OoKDg9GiRQv07t073/vq1auH2rVr49SpU0hPT4eJiYnaYxIREVEpIYioU6dOglQqFfbu3SsIgiBcu3ZNkEqlQv369ZViK1euLDRo0EDM4csVV1dXwdXVtfgdpMQKwlzLsvknJbZEX7vIyEjBxMREACBYWFgIfn5+wvr164XQ0FAhJycn3/vs7e2Fgv6TuHjxovDw4UOl9o0bNwoAhI4dOypdAyDY29vn22f79u0FAMKjR4+U7gMgNGzYUHj+/Lm8/eHDh0KlSpUEAEJERES+/b4rOztb2Lt3r5CVlaXQ/urVK6FZs2YCAOHMmTMK1+bOnSsAEDZt2qTWGKrek76+vnDo0CF5e1ZWljBkyBABgNC3b1+Fex4+fChcvHhRqa/Q0FChUqVKgqWlpZCcnCxvP3XqlABAaN68uZCenq5wT2JionDlyhWFtp49ewoAhC+//FLIyMiQt7948UL+NThy5IjCPfn9PKj6np4+fVr+PRs4cKDCGHv37hUACHXq1FHKs3r16oKenp6wbds2hWuXL18WrK2tBXNzc+HVq1dKOaiydu1aAYAwa9asAuOSkpKEPXv2CKNGjRJq1Kghz1tfX1/w8vISlixZIty8eVPlvT///LMAQPjoo49UXk9ISBAACJUrV1Yr58ePHwvGxsaCgYGBcPToUXn7uz8rBf0cfvzxxwIA4dSpU2qNR0RUUTyNSxXsZxxU+JOSka3rtIiUiLpMd/To0RAEAZ999hk8PT3h5eUlb3/XrVu3EB8fj0aNGok5PBGAtzMmBw4cQO3atZGcnIwtW7ZgzJgxaNq0KWxtbTFu3Dg8f/68yP22bNkSDg4OSu0jRoxAmzZtEBgYKPpxMb/88ovCbKSDgwOGDh0KAAgKClKrD319ffTr109pJsvOzg6LFy8GAI084/fpp5+iZ8+e8tcGBgb4+eefYWpqiv379+Pp06fyaw4ODmjZsqVSH02aNMH48eORlJQkX2UB/G8pdps2bZRmAS0tLeHp6Sl/ff36dRw+fBjNmzfHypUrFVZkVK1aFX/88QcAYO3atSV8x2/HXr16tcIY/fr1g7u7O548eaKwHHvjxo14/vw5vvrqKwwZMkShn2bNmmHOnDlISUlRewY8bzbS2dlZ5fWYmBh06tRJvgx2w4YNyMnJga+vL3bu3InY2FicPXsWM2bMyPfv5rwzok1NTVVez9tJPTk5Wa2cN27ciIyMDAwaNEhhJvvdn5WC5K0yuH79ulrjERERUeki6jLdQYMG4ebNm1ixYgWuXbsG4O0yw6lTpyrE5T2j5e3tLebwRHLe3t6IiIjAoUOHcPz4cVy6dAk3b95EQkIC1q5diz179uDs2bP5/uKen5SUFBw4cADXr19HXFwcsrOzAbzdxVQQBERGRsqXKpaUgYEBOnbsqNTu5OQkH7Morl+/juPHjyMqKgppaWkQBEFeNISHh5c84fe8v8QTAGxsbNC1a1f897//xblz5zBo0CD5tdzcXAQEBODChQt4/vy5/LnYvNzezfGDDz6AVCrFpk2b4Orqiv79+8PGxkZlHsePHwfwtiiUqlj+nfcMqRhHknh6eqrMw8nJCbdv38bz58/lz5vm5dW/f3+VfeV9mKduXq9evQIAWFtbq7weGxurUNC3bdsW3333Hdq3bw9DQ0O1xhBb3gcqhf2s5Kdy5coA/vfhBBEREZUtohajALB48WJMnToVERERqFOnDqpXr64U07NnT3h5ecl/2SLSBENDQ3z00Uf46KOPALx9nm/nzp2YNWsWXr16hQkTJuS7OY4qp06dwsCBAwv8xVfdGSF1VKtWDXp6ekrtFhYWAKD2JkZZWVkYPnw4duzYkW+MmHnnsbe3V9meV4w9e/ZM3hYdHQ0fHx/5M4mqvJujk5MTli1bhm+++Qb/+c9/MHbsWLi7u8Pb2xvDhw+XP7sLQD4bOXv2bMyePTvf/jMyMtR5WwV69znYd6n6nuXl1aZNmwL7fP36tVpj583K5431PicnJ/z55584dOgQTp48iXPnzqFr164wMzODt7c3evbsiZ49eyrtiP6uvA3q0tLSVF5PTU0tMIf35f0MFPazkh9LS0sAb//bJiIiorJH9GIUePuJdn6zFADQqVMnTQxL7zKpDEyL1HUWxWNSWSPdVqpUCWPHjkWNGjXQt29fnD59GmlpaYUuBQTezoh++umniIuLw3fffYeBAwfC3t4eJiYmkEgkGDx4MHbs2AFBEETLV9UsXnGsXLkSO3bsQKNGjbBs2TI0bdoU1tbWMDAwwIMHD+Ds7Cxq3sUxevRo3LhxAx9//DGmT58OZ2dnWFhYQCqV4o8//sDnn3+ulOOUKVPw6aef4r///S9OnDiBoKAgrFq1Cj/99BNWrVqFL7/8EgDkuwW3bdtW40eyFOV7lpfXgAED5MtbVVF3wysrKysA+X+wYGpqilGjRmHUqFHIzs5GUFCQfOOi/fv3y49IcXNzkxembdq0UVjeXadOHQBQ2qguT157fsWl2PIK8NJwZjAREREVnUaKUSoFpFLAzLbwuAoo78OQ3NxcJCQkqFWMBgUF4c2bNxgwYID82JN3PXz4UPQ8xbJ3714AwI4dO+Dm5qZwTZN5R0VFKcxQvtsOADVq1ADwdjbtxIkTqFq1Knbt2qU0G1xQjrVr18bEiRMxceJE5OTkYOfOnRgxYgSmT58OX19fWFtby2cr+/XrhylTpoj19kqsVq1auH//PmbOnKnwjGtx5e0aHRdX+C7aBgYG6NSpEzp16oQVK1bg8ePH8sL09OnTWL58OZYvXw4XFxeFs0YbN24MALh9+zays7OVnkMODQ0FAJXfd1WqV6+O+/fvIyoqCq6urkrX835W8hMfHw9A/WOciIiIqHQpdjHq7+8P4O2n8X379lVoKwoxDmcnepcgCPIzPlWJiIgA8HYZ77tHt+Q9N5eTkwN9fcX/NPJ+6VW1DDMiIkL+S/j7DAwMkJOTU7Q3ILKCcv/7779V3vPu16K4/v77b6WjOeLi4nD8+HFIJBL58tTExETIZDJUr15dqRDNzs6WF9OF0dfXx9ChQ7FmzRpcvHgR4eHhaNGiBbp06YI5c+Zg7969paoY7dKlCwICArB3715RitG8QvH+/ftFvrdu3boYN24cxo0bh4yMDJw+fRqHDx9GWFiYQpyDgwMaNmyIu3fv4tChQ+jXr5/C9d27dwNAgUeyvMvLywuBgYH4+++/lc40zftZKUheofzBBx+oNR4RERGVMsXdhlcikQhSqVRo2LChUltR/pBqJT7apQKbPXu2MHXqVJVHn0RHRwstWrQQAAgDBgxQuJZ3JMnt27eV7rt8+bL8eI53j9qIj48X2rVrJz+C4vTp0wr32dvbC/r6+kJ8fLzKXAs62iW/I2E2bdokABDmzp2r8vr7evXqJQAQlixZotD+zz//CHp6egIAoX379irHmDp1qlpjvCvvPb1/XEd2drbg6+srABB69+6t0G5lZSXo6+sL586dk7fn5OQIkydPln9t332/p06dEk6cOCHk5uYqjJ139I1EIhFiYmLk7V26dBEACOPGjRMSExOVcr5+/booR7v4+fmp/Jr4+fkp/XzEx8cLVapUEQwMDITff/9d6b1kZ2cLR48eFW7duqWyz/fduXNHACB07txZrfjiWr9+vQBAaNCggfDy5Ut5+549ewQAgqOjo5Cdrd7xAQ8fPhSMjIwEAwMD4cSJE/L2rKws+c8KCjjapVatWoKhoaGQlpZWovdERFTe8GgXKiuKPTPq6+sLiUSisEFRXpsuBAYGYv78+bhy5Qr09PTQqlUrLFq0CE2aNFG7j99//x2//fYbwsPDYWVlBR8fHyxatEi+/C3P5s2bMWLECJV9VK1aFS9evCjRe6GSSUlJwc8//4wVK1bAyckJrq6uMDY2RnR0NEJCQpCdnQ1HR0f89NNPCvf16dMHZ86cgbe3Nzp27AgzMzPY2tpiyZIlaNasGbp06YITJ07AyckJHTp0APD2587W1hZ9+/ZVeTxKnz598Ouvv6Jp06Zo3bo1jI2N4ezsjGnTpmnhK/HW9OnTcfToUcycORP//PMPnJycEB4ejitXrmDq1KlYsWKF0j1du3aFsbExVq1ahdu3b6NGjRqQSCSYNm2a2jsQ/+c//0GPHj3Qrl07VK9eHSEhIXj06BFq1KiB1atXy+P09fUxffp0zJ49G+3bt0enTp1QuXJlhISE4OXLlxg/fjx+++03hb5v3LiByZMnw87OTr6DbWxsLM6cOYPMzExMnDhRvgwYALZt24bu3btjzZo12L59Oz744APUqFEDiYmJuHnzJp4+fYovv/wS3bt3L+ZXuegqVaqEffv2oXfv3vj888/x/fffw93dHdbW1njx4gVCQ0ORkJCAvXv3wt3dvdD+GjZsCAcHB4SEhCAjI0PpyJt79+4pzWQWpmbNmggICFBoGzlyJA4fPoy9e/fCxcUF3t7eeP36Nc6cOQMTExNs27ZNaWVBfhwcHPDjjz9iwoQJ6NatG9q1a4dq1aohODgY8fHxGDJkCP766y+V90ZGRiI6Ohrdu3eHiYlJkd4XERERlRK6robFcPDgQUEqlQrW1tbC+PHjhfHjxwvW1taCiYmJcOnSJbX6mDFjhgBAcHJyEqZNmyYMGjRI0NPTExwdHYW4uDiF2LxZo759+wpz585V+LN8+XJR3hNnRosvNjZW2Lp1qzB06FChUaNGgo2NjaCvry9UrlxZaNOmjbBs2TIhJSVF6b7s7Gzh22+/FerXry8YGBgozYClpaUJs2fPFho0aCAYGRkJtWvXFsaOHSu8fv1a5cyXIAhCSkqKMGHCBKF27dqCvr6+0iykNmZGBUEQLl68KHTq1EmwtrYWLCwshNatWwt79uwRHj16pHJmVBAE4dixY0KbNm0Ec3PzfGd+VXn3PW3atEn44IMPBGNjY8HGxkYYNmyY8PTpU5X3bdmyRWjSpIlgamoq2NjYCH379hVu3Lih8v2Gh4cL3377rdCmTRuhevXqgqGhoVCzZk3B29tb2LNnjyCTyZT6T09PF3755RehdevWgpWVlWBoaCjUrl1baN++vbB8+XKlvDQ9M5rn+fPnwvTp0wU3NzfB1NRUMDU1FerXry/07dtX2Lx5s5CcnKyyT1V++OEHAYDw999/K127du2a/Puo7p/8fgZzcnKEH3/8UXBzc5N/bwcMGCDcuXNH7VzftXfvXuHDDz8UTExMBGtra6Fv377C3bt3hblz5+Y7M7pgwQIBgLBnz55ijUlEVJ5xZpTKCokg6HgbzRLKyspC/fr1ERcXh9DQUPmszb179+Dp6QkXFxdcvXq1wD5u376Nxo0bw83NDcHBwfINbfz9/eHn54fJkydj5cqV8vi8mdFNmzZh+PDhGnlfeRvN3LlzRyP9E1H58+LFCzg4OKBz5844cOCArtPRGEEQ0LBhQ6SkpODx48dqz8QSEVUU0fFpaLv0tELbnfndYGbEvy+pdBHn7Ag1yWQy0Q8nP3HiBKKjozFs2DCF5YMuLi4YMmQIQkNDce3atQL72LJlC2QyGWbNmqWws6qvry+cnJzg7++v801oiIgKU61aNUycOBGHDh3CrVu3dJ2Oxvz3v//F/fv3sWDBAhaiREREZZioxej9+/fxyy+/ICgoSKE9KysLEydOhLm5OapVqwYHB4dCd0lUV95Y3t7eStc6d+6sEFOcPry9vfHmzRulXSWBt8cY5B2BcODAAWRkZBQ5fyIiMc2aNQvW1tYqjyAqDwRBwIIFC+Du7q6xlSlERESkHaJ+pLxmzRqsXr1afnh6nvnz5ytsQBIVFYV+/fohNDRU7QPd85N3TIeqw+zz2vJiCurDwsJC5Vl17/bx/tl5v/76q8Lr6tWr46+//kLHjh3Vzv/9cx/zREZGqnxPREQFqVSpEt68eaPrNDRGIpEUutqFiIiIygZRZ0bPnj0LY2NjhR0ps7KysGbNGhgYGODvv//G06dP8Z///AcZGRlYtWpVicdMSkoCAFhaWipdy2tLTEwstA9V9+fXR7169bBu3TpERkYiPT0dkZGR+P777xEfH4/evXsXWvwSERERERFVdKLOjD5//hw1a9ZUOLj+woULSExMRP/+/TFgwAAAwKpVq/DXX3/h1KlTYg6vNe3atUO7du3kr+vVq4fZs2ejUqVKmDBhAlauXIk1a9ao1Vd+GxTlN2NKRERERERUHog6MxoXFwdra2uFtnPnzkEikaBHjx7yNhMTEzg6OiI6OrrEY+bNXObNkL4rr83KyqrQPlTdX5Q+AGD48OGQSCQIDg4uNJaIiIiIiKgiE7UYNTMzw4sXLxTazp49CwBo27atQruBgYEouyA6OjoCePuM5fvy2vJiCuojOTlZ5U6/6vYBAKampjAxMUFqamqhsURERERERBWZqMWoq6sroqOjcf78eQBvNyoKDAxEtWrVFI5dAYCnT5+iSpUqJR7Ty8sLABAQEKB07eTJkwoxxekjICAANjY2cHV1LTSXu3fvIi0tDfb29oXGEhERERERVWSiFqPDhg2DIAjw8fHBxx9/jDZt2iA3Nxe+vr4KcZGRkXj58mWJd9IF3h7fUqtWLWzduhX379+Xt9+7dw9//fUXmjRpgiZNmgAAsrOzce/ePTx58kShDz8/P0ilUixatAhpaWnydn9/fzx48AC+vr4Ks7jXr19XyiMhIQGff/45AOCzzz4r8fsiIiIiIiIqz0TdwOg///kPzpw5g127dmHv3r0A3i7PnT17tkLctm3bAKg+17OojIyMsHbtWvTt2xetWrXC4MGDAQDbt2+HTCbDunXr5LExMTFo2LAh2rdvj8DAQHm7u7s7pk6dimXLlqFJkybo27cvYmJisGvXLjg6OmLOnDkKY3bs2BH29vbw8PBAjRo18Pz5cxw5cgSxsbHo27cvz74jIiIiIiIqhEQQBEHsTq9fv4779++jTp06aNmyJSQSicL1v/76C69fv8aAAQNQs2ZNUcY8ffo05s+fjytXrkAqlaJNmzb44Ycf0LRpU3nM48eP4eDgoFSMAm8PUv/jjz+wevVqhIeHw8rKCj4+Pli8eLHScuLvvvsOp06dQkREBOLi4mBqagp3d3f4+flh1KhRkEpLPuGct5tufrvtEhERERGpEh2fhrZLTyu03ZnfDWZGos5DEZWYRopRKjkWo0RERERUHCxGqawQ9ZnRJUuWKO2mS0RERERERPQ+UYvRWbNmoU6dOujbty/279+P3NxcMbsnIiIiIiKickLUYrR3794AgAMHDuCjjz5CrVq1MGPGDIVdbomIiIiIiIhELUb37duH6OhoLF26FM7Oznj58iVWrFgBV1dXeHl5YfPmzQpHpxAREREREVHFJGoxCgBVqlTBtGnTEBYWhgsXLmDkyJEwNzfH+fPnMWrUKFSvXh1jxozBxYsXxR6aiIiIiIiIygjRi9F3tWzZEuvXr8eLFy+wefNmeHl5ISUlBRs3bkTbtm3h6uqKlStXIjY2VpNpEBERERERUSmj0WI0j4mJCXx9fXHy5EksW7YMenp6AIB79+5h2rRpqF27Nnx9fREZGamNdIiIiIiIiEjHtFKM3rt3D9OnT0ft2rUxffp05OTkwMrKCl988QX69esHQRCwbds2eHh44Ny5c9pIiYiIiIiIiHRIYyffpqSkYOfOndi4cSNCQkIgCAIAoG3bthgzZgw++eQTGBsbAwBevnyJ2bNnY+PGjZg5cyYLUiIiIiIionJO9GI0KCgIGzduxO7du5GWlgZBEGBraws/Pz+MHj0azs7OSvdUrVoVf/75J06dOoXr16+LnRIRERERERGVMqIWo87OzoiIiIAgCJBIJPD29saYMWPQr18/GBgYFHp/nTp1EBUVJWZKREREREREVAqJWoyGh4ejRo0aGDFiBEaNGoW6desW6f5vvvkGI0aMEDMlIiIiIiIiKoVELUb37duHXr16QSot3r5I3bp1EzMdIiIiIiIiKqVELUZ79+4tZndERERERERUTmlsN10iIiIiorLk7ytP8ePx+3iZlKnrVIgqhGIXo506dSrx4BKJBAEBASXuh4iIiIioJBLTszFzz03IBF1nQlRxFLsYDQwMLPHgEomkxH0QEREREZXU49ep5bYQrWRqAGMDPV2nQaSk2MXo6dOnxcyDiIiIiEhnZEL5rET1pRLM6O4CPSkngaj0KXYx2r59ezHzICIiIiLSGVWl6PHJ7bSeh9hqVjKBmRG3iaHSiT+ZRERERFThvT8xamaoB6eqFrpJhqiCKN6BoERERERE5YpiNcq9TYg0T2Mzo5cvX8a1a9fw5s0bZGdnq4yRSCSYM2eOplIgIiIiIlLL+zOjLEWJNE/0YvTSpUsYPnw47t+/L28TBEHp06W8NhajRERERKRrSs+Mshol0jhRi9HIyEh06dIFaWlpGDx4MM6ePYvo6GjMmTMHb968QXBwMEJDQ2FiYoJx48bB3NxczOGJiIiIiIpF9t65LlIu0yXSOFGL0aVLlyIlJQVr1qzB559/Di8vL0RHR2P+/PnymFOnTmHQoEE4efIkzp8/L+bwRERERETF8v7MKGtRIs0TdQOjgIAAWFhYYNSoUfnGdOrUCbt27cKNGzewaNEiMYcnIiIiIioWPjNKpH2iFqPPnj2Dvb099PXfTrjm/W9mZqZCXIcOHeDg4IA9e/aIOTwRERERUbEI3E2XSOtELUZNTU3lBSgAWFlZAXhbpL6vUqVKePLkiZjDExEREREVD2dGibRO1GK0Zs2aeP78ufy1i4sLAODMmTMKcYmJiXjw4AGMjIzEHJ6IiIiIqFj4zCiR9olajLZo0QKvXr3CmzdvAAD9+vWDIAiYMWMGjh49itTUVERERGDIkCFIS0uDl5eXmMMTERERERVLfFqWwmsu0yXSPFGL0T59+kAmk+HgwYMAgJYtW2LQoEGIjY1Fr169YGlpCWdnZxw+fBimpqZYuHChmMMTERERERVZRnYuJmy/ptDGUpRI80Q92sXHxwdPnz6FhYWFvG3Lli1wc3PD1q1b8ejRI5iamqJdu3ZYsGABPDw8xByeiIiIiKjIbjxNUGoz0BN1zoaIVBC1GJVKpahZs6biAPr6mDVrFmbNmiXmUEREREREokjNylFqa1XfRgeZEFUs/MiHiIiIiOg9C/q66ToFonKPxSgRERER0TtqWZvA1FDUBYREpEKx/ytbsGABAMDW1hbjxo1TaFOXRCLBnDlzipsCERERERERlVESQRDeP1ZJLVKpFBKJBM7OzggLC1NoK6zLvBiJRILc3NziDF/uubm9XRpy584dHWdCREREVL6duvcSIzdfkb+uZW2CczM66TAjooqh2DOjc+fOBfB2ZvT9NiIiIiIiIqKClLgYLayNiIiIiIiI6H3cwIiIiIiIiIi0jsUoERERERERaV2xl+mePXtWlATatWsnSj9ERERERERUdhS7GO3QoQMkEkmJBpdIJMjJySlRH0RERERERFT2FLsYbdeuXb7F6IULF5CdnQ0DAwPUrFkTVatWxcuXLxETE4Ps7GwYGhqiVatWxU6aiIiIiIiIyrZiF6OBgYFKbTKZDAMGDICBgQEWLlyIsWPHwtLSUn49OTkZa9euxffff4/KlStj9+7dxR2eiIgof4IAnFsF3DsE5GTqOhsiAoAh/wCW1QsNi45Pw5O4NC0k9D93nydrdTwieqvYxagqP/74I/bt24dDhw6he/fuStctLCwwffp0eHh4oGfPnvjxxx8xdepUMVMgIiICbv0DBMzXdRZE9C5ZdqEhvwSEY+WJB1pIhohKA1F3092yZQscHBxUFqLv6t69O+rVq4ctW7aIOTwREdFb0Zd1nQERFVFWjgxrAyN1nQYAoITbohCRmkQtRh8+fIjKlSurFVu5cmU8fPhQzOGJiIjeEmS6zoCIiigtKwfp2bm6TgMA4FLNsvAgIioxUZfpmpub486dO0hMTISVlVW+cYmJibhz5w7MzMzEHJ6IiEg1h/ZAk6G6zoKoYjNRb8LifTZmhiInkj+JBHCrYYUFfd20NiZRRSZqMert7Y2///4bvr6+2LZtGywsLJRiUlJS4Ovri4yMDPTp00fM4YmIiFSzcQQ8PtV1FkRURJdmeaOKpbGu0yAiDRG1GF24cCEOHz6MgwcPok6dOvD19YWrqyuqVKmCV69eISwsDFu3bkViYiLMzc2xYMECMYcnIiIiojIkVybgwctkZObIkJxR+AZHRFS+iFqMOjo64uTJkxg4cCAePXqE1atXK8UIgoC6detix44daNCggZjDExEREVEZ8TIpA5/9fhGP32j3GBciKj1ELUYBoHnz5ggLC8OuXbtw5MgR3Lt3DykpKTA3N4eLiwu6d++Ozz77DMbGXHJBREREVFH9ffkpC1GiCk70YhQAjIyM4OvrC19fX010T0RERERl3KvkzAKvVzYzhK25kZayISJd0EgxSkRERERUVCYGegCAOpVNMcfHFVIpD/wkKs9YjBIRERGRzg1raY+F/dx1nQYRaRGLUSIiIiLSGkEQsPpUBLYGR+k6FSLSMamuEyAiIiKiiuP0/Vf48cQDXadBRKUAi1EiIiIi0pqwZ0kq223MDbWcCRHpGotRIiIiItIaQVBuq2JhhI+b1tJ+MkSkU8V+ZjQpKQn6+vowNTUVMx8iIiIiqkCsTAxwZlpHmBjq6ToVItKyYs+MWltbo0ePHgptCxYswObNm0uaExERERGVUw9fpyq8/tChMgtRogqq2MWoIAgQ3ltnMW/ePGzcuLHESRERERFR+bT3WoyuUyCiUqLYxaipqSlev34tZi5EREREVI5l5ciU2nJkKh4iJaIKodjFqIuLC+7fv4+ffvoJ9+/fx5MnTwAAmZmZePLkidp/xBIYGIiOHTvCwsIClSpVQo8ePXDt2rUi9fH777/Dw8MDJiYmqFatGkaPHo1Xr14Vet/Tp09haWkJiUSCmTNnFvctEBEREZVrApQLz74f1NBBJkRUGhR7A6MJEyZg5MiRmDJlCqZMmSJvv3LlChwcHNTqQyKRICcnp7gpyB06dAh9+vSBlZUV/Pz8AADbt29HmzZtcObMGTRv3rzQPmbOnImlS5fCyckJEydORHR0NDZv3owzZ87g0qVLsLa2zvfezz//HDKZ8id9RERERFSwxrUq6ToFItKRYhejw4cPBwCsXr0ad+/eRXp6OiQSidJzpAUpSmx+srKyMHbsWBgbG+PixYtwdnYG8LZY9vT0xNixY3H16tUC+7h9+zaWL1+ORo0aITg4WL5DcPfu3eHn54eFCxdi5cqVKu/dunUrjh8/jiVLlmDatGklfj9EREREZVVKZg7m7b+D0CfxkKlYfssVuUT0rhKdMzp8+HBcuXIFqampkMlkEAQBbdu2hUwmU/tPSZ04cQLR0dEYNmyYvBAF3i4jHjJkCEJDQwtdrrtlyxbIZDLMmjVL4agaX19fODk5wd/fX+UM7suXL/HVV19h0qRJaNasWYnfCxEREVFZtvzoPey+Go2Hsal4/CZN6c+TuDRdp0hEpUiJitH31alTB9WrVxezy0IFBQUBALy9vZWude7cWSGmOH14e3vjzZs3CAsLU7o2YcIEWFhYYOHChUXOO4+bm5vKP5GRkcXuk4iIiEgXbsYkFileIgEqmRpoKBsiKu2KvUxXlcePH4vZnVoiIiIAAPXr11e6lteWF1NQHxYWFrCzsyuwDw8PD3n7v//+i927d+Pw4cMwMzMrdv5ERERE5UVRn8D6rFltVDI11EwyRFTqiVqMvi8iIgL37t1DcnIyLCws4OLiAkdHR1HHSEpKAgBYWloqXctrS0ws+FO6pKQkVKlSReU1VX3Ex8dj/PjxGDRoEHr06FGsvPPcuXNHZbubm1uJ+iUiIiLStsz3jm7xdqmCT5vXVhlbs5IJXKsr//5GRBWHRorRLVu24Pvvv8fDhw+Vrjk6OuLbb7/FsGHDNDG0Vnz99dfIysrCTz/9pOtUiIiIiEqNu8+TFF53dauKbm7VdJQNEZV2oj4zCgCTJk3CyJEjERkZCUEQULlyZbi6uqJy5coQBAHh4eEYPnw4Jk2aJMp4eTOXeTOk78prs7KyKrQPVfer6uP06dPYvHkzVqxYke9sKhEREREBSeklP8KPiMovUYvRAwcOYPXq1dDT08OUKVPw7NkzxMbG4tatW4iNjcWzZ88wdepU6Ovr47fffsPBgwdLPGbesl9VG/7ktRW2NNjR0RHJycmIjY0ttI8bN24AAEaOHAmJRCL/07FjRwDA0qVLIZFI8NVXXxXvDRERERGVUdbvbUZUo5KJjjIhorJA1GW6a9euhUQiwcaNGzF06FCl69WqVcOyZcvQuHFjDBs2DGvWrIGPj0+JxvTy8sLSpUsREBCATz75ROHayZMn5TGF9RESEoKAgAAMHDhQ4VpAQABsbGzg6uoKAHB3d8eoUaOU+nj+/DkOHz4MDw8PNG/eHK1atSrJ2yIiIiIqFaLj03Ah4g0ycnILjc3IVnxm1NacmxMRUf4kglDUfc/yZ2dnB2NjYzx9+rTQ2Nq1ayMjI0PlbGRRZGZmwtHREXFxcQgNDZWfNXrv3j14enrC2dkZoaGhAIDs7GxERkbC1NQUderUkfdx+/ZtNG7cGG5ubggODpafNerv7w8/Pz9MnjwZK1euLDCPwMBAdOzYETNmzMCSJUtK9J6A/21glN8GR0REVIBDU4DLf/7vdbNRgE/Bf48TkbL7L5Lx8doLSMks3nLbXf9piQ/r2YicFRGVF6Iu001OTkaNGjXUiq1RowaSk5NLPKaRkRHWrl2LjIwMtGrVChMmTMCECRPQunVryGQyrFu3Th4bExODhg0bwtfXV6EPd3d3TJ06Fbdu3UKTJk0wffp0DBkyBCNHjoSjoyPmzJlT4jyJiIiIypqDN58VuxAFAD2pRMRsiKi8EbUYtbOzQ3h4OLKzswuMy87ORnh4uMpzPYvDx8cHJ0+ehIeHBzZv3gx/f398+OGHOH/+PFq0aKFWH0uWLMG6detgaGiIX375BSdPnoSfnx/Onz8Pa2trUfIkIiIiKkuSM4pfiJob6aMhj24hogKI+sxou3btsHPnTsyePRvLli3LN2727NlISEhAr169RBu7Y8eO8k2E8lO3bl3ktypZIpHg888/x+eff16s8Tt06JBv30REpGW3/9V1BkRlWkxCOr7edR0hj+IU2u0sjFDP1qzQ++0sjDCqrQPMjDR6pD0RlXGi/g0xffp0/PPPP/jxxx9x8eJFfPnll3B3d0fVqlXx8uVL3L59Gz/99BMuXrwIAwMDTJs2TczhiYiIAEEA0uPeb9RJKkRl1Q+HwpQKUQDo3LAqFvdvpIOMiKg8ErUYbdy4MTZs2IAxY8bg/PnzuHDhglKMIAgwNDTE+vXr4eHhIebwREREQG6WcptET/t5EJVh4S9TVLZXtTTSciZEVJ6JvnZi2LBh8PT0xPLly3H06FG8fPlSfq1q1aro0aMHpk6dKj8qhYiISOOaDtN1BkSlXnR8GnZeeopXyRl4kZShdN3exhSfNa+tg8yIqLzSyEJ+V1dXbNq0CQCQlJSE5ORkWFhYwNKSD7ETEZEOGJrrOgOiUi07V4bPfg9GTEK6yuuTvBvgS+8G3B2XiESl8afKLS0tWYQSERERlWL3nifnW4gCQONaVixEiUh03OKMiIiIqJyTyQRciHyDR29SVV5/kk87ANiaG6GZfWVNpUZEFRiLUSIiIqJybv6BO9hyMapI94xq6wBrUwP0aVwTVqYGGsqMiCoyFqNERERE5VhOrgy7rjwt0j1WJgaY48PNJolIs6S6ToCIiIiINCc7V0BGtqxI9zSvy2W5RKR5nBklIiIiKsdyBUGprWYlE9SsZKIy3rmaBaZ0ddJ0WkRELEaJiIiIyjOZimJ09xetUN1KdTFKRKQtLEaJiIiIyokHL5Ox7kwknidkyNtyZMpLdPUkPKaFiHRPo8WoIAh48+YN0tLSUKdOHU0ORURERFSh5coEDNsQgpdJmYXGSliMElEpoJENjAICAtC9e3dYWFigatWqqFevnsL1pUuXYuTIkYiLi9PE8EREREQVTvirZLUKUT2pBGZGelrIiIioYKIXowsWLEDXrl1x/PhxpKWlQRAECO89q2BlZYUtW7Zg//79Yg9PREREVOH8feUpuv8UpFZs38Y1YGrIJ7WISPdE/Zvo2LFjmDdvHiwsLPD999+jX79+GDRoEC5evKgQ179/f4wbNw7//e9/MXz4cDFTICIiIqpQcnJl+P5gmMprc3u74t0FuXVsTOHVwE47iRERFULUYvSXX36BRCLB5s2b8dFHHwFQ/UxClSpVULt2bVy/fl3M4YmIiIgqnOSMHCRl5Ci129uYYkQbBx1kRESkHlGX6V66dAl2dnbyQrQg1apVw8uXL8UcnoiIiKjCUXWOqKG+FD/0a6SDbIiI1CfqzGhycjLc3d3Vis3NzYVUqpH9k4iIiIgqDJlMuRgN+cYb1maGOsiGiEh9olaDdnZ2ePz4caFxOTk5ePDgAWrWrCnm8EREREQVjopaFFIpj24hotJP1GK0TZs2iI+Px4EDBwqM27p1K1JSUtCxY0cxhyciIiKqcFQt02UtSkRlgajLdCdNmoS///4bn3/+OaysrNCuXTulmH///Rdffvkl9PT0MHHiRDGHJyIiIirXrjyOw5WoeOS+Mx2alJ6tFKfHapSIygBRi9HWrVtjzpw5WLhwITp27AhHR0e8efMGANC7d2/cuXMHUVFREAQBS5cuVfv5UiIiIqKKbt/1GHy587pasVIVpxkQEZU2op94PH/+fNSrVw/ffvstwsPD5e2HDh0C8HYX3SVLlsDX11fsoYmIiIjKra0Xo9SKk0g4M0pEZYPoxSgA+Pn5YciQIQgODsbNmzeRkJAAc3NzuLu7o23btjA05O5uREREROoQBAHzD4ThSlS8WvEtHWxgoMcTC4io9NNIMQoA+vr6aNu2Ldq2baupIYiIiIjKvQcvU7D5wmOV1zo3rKLw2sHWDGPb19dCVkREJaexYpSIiIiIiufeiyT8dCIc0QlpuB2TpDLmr9Efoo2jrZYzIyISj6hrOA4cOIB69ephxYoVBcYtX74c9erVw5EjR8QcnoiIiKjMEwQBozZfwdE7L/ItRId8WAct69loOTMiInGJWoxu27YNUVFR6Nu3b4Fxffr0wePHj7Ft2zYxhyciIiIq814lZyImIT3f605VzfHDR424SRERlXmiFqNXr16Fra0tGjRoUGCcs7Mz7OzscPnyZTGHJyIiIirzAu6+KvD66Lb1tJQJEZFmifrM6LNnz9Q+O9Te3h5hYWFiDk9ERERU5p2PfK3U9sNH7jAx0IN7TSs4VbXQQVZEROITtRg1MjJCYmKiWrGJiYnQ09MTc3giIiKicmnIh/a6ToGISHSiFqMuLi64dOkSIiIi4OjomG9ceHg4wsPD4enpKebwRET0rswU4NpW4E2krjPRLiFX1xkQlUhmtuLP8H/acVkuEZVPohaj/fr1Q0hICEaOHIkjR47AzMxMKSYtLQ2jRo2CRCLBRx99JObwRET0rj2jgQfctZyoLDkf8Ron33tmtLKZoY6yISLSLFGL0fHjx+P333/H+fPn4ebmhvHjx6Nly5awsrJCYmIiLl68iLVr1yIqKgp169bFpEmTxByeiIjy5GQB4cd1nUXpoW+s6wyI1LLo8F2lNn3umktE5ZSoxai5uTkOHz4MHx8fPHz4EDNnzlSKEQQBjo6O2L9/P8zNzcUcnoiI8shyuFw1T/UPAMsaus6CyqFXSRmYsecmbkYnQhCpz7jULKW2+nb8fYmIyidRi1Hg7XOjN2/exO+//469e/ciLCwMSUlJsLS0hLu7O/r374/Ro0fD1NRU7KGJiKggjT4FjCrYLpxWtYCmvoCEM0skrsS0bEzccQ0hj+I0Ok49OzN4NbDV6BhERLoiEQRBrA/zSERubm4AgDt37ug4EyIqk7LSgEXVFdu+vAFY19VJOkTlRWJ6Nv7jf0XjRWieU1Paox5nRomonBJ9ZpSIiIiovMnJleFGdCIWHb6Lq1HxWhmzd+MacLBV3gySiKi8YDFKREREVABBEDB221WlXW7f922vhnCrYSXKmHYWRqhvZwYJl5gTUTmmkWI0ICAAhw4dQmRkJFJSUpDfSmCJRIKAgABNpEBERERUYskZ2Vh85F6BhaihnhTDWtljVFsHFo9EREUgajGamZmJTz/9FAcPHgSAfIvQPPwLm4iIiEqzcX+FIij8db7XZ/V0gW+rujA20NNiVkRE5YOoxeiCBQtw4MABmJmZYdSoUWjZsiWqVq0KqVQq5jBEREREGhGbnInv9t3G7WeJSM3MVXnUCgC4VLNAV9eqGN22HqQ8B5SIqFhELUZ37twJqVSKw4cPw8vLS8yuiYiIiDQqIS0L/X47j5iE9ALjhrW0x8J+7lrKioio/BK1GI2JiYGDgwMLUSIiIipT/gqJwrf/vY3CDrz7tldD+LWuq5WciIjKO1GLUTs7O1haWorZJREREZHGvEjMwJ7QaCw/dr/Q2DFeDhjtVU8LWRERVQyiFqM+Pj7YtGkT4uLiULlyZTG7JiIiIhJVdq4Mn/5+EU/i0vKNmdfbFXVsTFHb2hSOVcy1mB0RUfkn6s5C8+fPh62tLUaOHInU1FQxuyYiIiISTWxyJlr8cDLfQlQiARb0dcPwNg7o5FIVDapa8BQAIiKRiTozevToUYwdOxYLFy6Eo6MjBg0ahAYNGsDMzCzfe3x9fcVMgYiIiKhQ03ffQHxatsprDatb4vCktiw+iYg0TCIUdhhoEUilUkgkEvn5our8JZ6bmyvW8OWKm5sbAODOnTs6zoSIyqSsNGBRdcW2L28A1nV1kg5RaRLxKhmdV55VeW1qVyd81rwO7CyMtJwVEVHFI+rMaLt27fgpIhEREZVae65GY8o/N1ReWzOkKXo2qq7yGhERiU/UYjQwMFDM7oiIiIiKRBAERL1JQ3xaltK1K4/j8cPhuyrv2zuuNZrUsdZ0ekRE9A5Ri1EiItICWS5wbSvw7DqAfJ60yM3RZkZEpUJOrgyfb72KgHuvinSfg60ZC1EiIh1gMUpEVNacWgicW6XrLIh0Li41Cz+ffIDI2Lc7+F+JikNGtqxIfVgY6WPdUE9NpEdERIXQSDH65s0brF+/HoGBgYiOjkZ6ejoiIyPl1w8dOoQ3b95g4MCBMDQ01EQKRETl173DxbtP30TcPIh0bPxfobj48E2x769ZyQR7vmiNalbGImZFRETqEr0YPXbsGIYMGYL4+Ph8d9W9fPkyFi5ciMqVK8PHx0fsFIiIyrfczKLfU6sFYFFV/FyIdCQ1M0etQtTUUE+pTV8qQRtHWywb4AELYwNNpEdERGoQtRi9f/8++vfvj/T0dHz00Ufo168fli1bhrCwMIW4gQMHYsGCBdi7dy+LUSKikrKqAzj3yP96pdrAB0O0lw+Rhj2NS4PXstOFxi36qBEGf1hHCxkREVFxiFqMLl68GOnp6Zg7dy7mzp0LAPjjjz+U4lxcXFC5cmVcuHBBzOGJiCqmHksBl566zoJIY57GpeF8xGtk5rx9HnTuftVncE/v7gwjfT1IJYBHrUrwtOemREREpZmoxWhAQAAsLCzw7bffFhprb2+P+/fvizk8ERERlTP3XySj1y9ByJHls3P0/zMx0MO4Do5ayoqIiMQgFbOzV69ewdHREXp6ys9nvM/AwAA5OTx6gIiIiFQLfRKPbj+dLbQQBYD1vs20kBEREYlJ1JlRS0tLvHql3tleUVFRsLOzE3N4IiIiKuVuxyRi5+UniE/NLjT20K3navV5bkZH1LI2LWlqRESkZaIWo02aNEFAQADu3LkDNze3fOPOnj2Lly9fon///mIOT0RERKVQelYuLkS+RsSrFCw+cq/E/bWsVxkAUM3SGP9pV5+FKBFRGSVqMern54eTJ09izJgxOHDgAGxsbJRioqOjMXr0aEgkEowYMULM4YmIiEjHBEHAi6QM5P7/0tpcmYBP1l3Eq+RiHEn0HgM9CYK/8YaNuVGJ+yIiIt0T9ZnRwYMHo3v37ggODoabmxs+//xzxMTEAAB+/PFHjBgxAg0bNkRERAQ+/vhj9OrVS7SxAwMD0bFjR1hYWKBSpUro0aMHrl27VqQ+fv/9d3h4eMDExATVqlXD6NGjVS47DggIwMCBA+Hk5ARLS0tYWFigUaNGmDNnDuLj48V6S0RERGXKw9gUtFt+Gq0Wn0LbpafRdulptF8eKEoham1qgHsLe7AQJSIqRySCIBS+K0ARpKenY9y4cfD398e7XUskEvnr4cOHY+3atTAyEucflEOHDqFPnz6wsrLC4MGDAQDbt29HRkYGzpw5g+bNmxfax8yZM7F06VI4OTmhb9++iI6Oxt9//w0HBwdcunQJ1tb/2x7+m2++wc6dO9GyZUvUrFkTMpkMly9fxrlz51C3bl1cuXJF5axwUeQtc75zR/X29URUgf3cGIh//L/XA3fwaBfSuHPhr7H8+H08T0jPN6Y4Reck7waFxlSxMEKvRtVhbWZY5P6JiKj0Er0YzXP37l3s2bMHN2/eREJCAszNzeHu7o4BAwbAw8NDtHGysrJQv359xMXFITQ0FM7OzgCAe/fuwdPTEy4uLrh69WqBfdy+fRuNGzeGm5sbgoODYWr69tkTf39/+Pn5YfLkyVi5cqU8PiMjA8bGxkr9zJ8/H/PmzcOCBQswZ86cEr0vFqNElC8Wo6QlLxIz8MupcES8TMGlx3Gi9ftB7UrwqGWF6d1dYG4k6hNDRERUhmjsX4CGDRuqdd5oSZ04cQLR0dH4/PPP5YUoALi4uGDIkCFYv349rl27hiZNmuTbx5YtWyCTyTBr1ix5IQoAvr6++OGHH+Dv749ly5ZBX//tl0tVIQoAH330EebNm4eHDx+K9O6IiIi078yDWATef4VN5x+L2m9dG1OsHtwU7jWtRO2XiIjKpjL/cWRQUBAAwNvbW+la586dsX79egQFBRVYjBbUh7e3N9auXYuwsLBCZ3SPHTsGAAXuJExERFRaXY2Kx4/H7+NC5JsS91XP1gw7/9MSZv8/8ymRAKaGZf7XDiIiElGZ/1chIiICAFC/fn2la3lteTEF9WFhYaHy3NN3+3i/GD18+DAuXbqE1NRUXL9+HQEBAWjRogW++OILtfPPr3CNjIxU+Z6IiIjEFJOQjlvRCdh9NQYn775U6576dmaY1yf/D14tjA3gXsMS+nqi7pNIRETlTLGL0Xr16pV4cIlEgsjIyBL1kZSUBACwtLRUupbXlpiYWGgfVapUUXmtoD4OHz6M3377Tf66S5cu2L59O8zMzNRLnoiISIf2XovG5F031I6f29sV9ezM4eVoC6lUosHMiIioIih2Mfr48eMSDy6RlO1/yFavXo3Vq1cjISEBISEhmDp1Kpo1a4aTJ0/C0dFRrT7y26CIS32JdCT1DRB1HsjOf8dQnctM0XUGVA5sOv8I8w+EFRpXz9YMnzWvjU+b1eZutkREJKpiF6OPHj1S2b5nzx588803cHR0xMSJE+Hq6oqqVavi5cuXCAsLw+rVqxEREYHFixejf//+xU48T97MZd4M6bvy2qysCt4owdLSUuX96vZRqVIldOvWDQ0bNoSjoyO++uorHDx4UK38iagUiXsE/NkZSHut60yINCIjOxcyQcCuy08LLUTr2Znhq85O6NO4hpayIyKiiqbYxai9vb1S29mzZzFz5kyMGTNGYfkqADg7O6Ndu3YYO3YsJkyYgOnTp6NZs2Yq+ymKvBnIyMhING3aVOFa3hLgwmYpHR0dERISgtjYWKXnRtXtAwDq1KkDR0dH+YZIRFTG3NlbNgtRCZ/Lo4K9ScnEF3+F4tKjwo9n8bS3xpAP66B/01payIyIiCoyUX+DWbRoEczNzbFq1aoC41auXAlzc3MsWrSoxGN6eXkBAAICApSunTx5UiGmOH0EBATAxsYGrq6uheYiCAJevHghPwKGiMqYdPHOUdQaqT5QvbGus6BSbtLOa4UWopM6OeLR4p7Y80VrFqJERKQVolZNly9fRoMGDWBoWPAzJYaGhnBycsKlS5dKPGbnzp1Rq1YtbN26FZMnT5afNXrv3j389ddfaNKkifxYl+zsbERGRsLU1BR16tSR9+Hn54eVK1di0aJF6NOnj/ysUX9/fzx48ACTJ09WKDBDQ0OVZmEFQcCCBQsQHx+PgQMHlvh9EZEOCILia+NKQKXaOklFLaY2QMvxgGV1XWdCpdTrlEzM+vcWzkcUfFTL9/3cMbRlyVYqERERFZWoxWhmZiZiYmLUio2JiUFmZmaJxzQyMsLatWvRt29ftGrVCoMHDwYAbN++HTKZDOvWrVMYs2HDhmjfvj0CAwPl7e7u7pg6dSqWLVuGJk2aoG/fvoiJicGuXbvg6OiIOXPmKIzp7e2NWrVqoXHjxqhVqxbi4+Nx/vx53LlzB3Xq1MGyZctK/L6ISAfeL0Yb+gB9f1MdS1QGfLLuIh69Ti0whoUoERHpiqjLdN3d3fH8+XP8+eefBcZt2LABz549g7u7uyjj+vj44OTJk/Dw8MDmzZvh7++PDz/8EOfPn0eLFi3U6mPJkiVYt24dDA0N8csvv+DkyZPw8/PD+fPnYW1trRA7d+5cVK9eHadPn8bKlSvx119/QV9fH99++y2uX7+O2rVL8UwKEeVPkCm+5rOYVIYlpmXnW4huGtEcQdM74t7C7ixEiYhIZySC8P5UQPHt2LEDQ4YMgZ6eHnx9fTFhwgS4urrCyMgImZmZCAsLw2+//YYtW7ZAJpNh27ZtGDRokFjDlyt5R7vkd/QLEWnAkRlAyP9WU8BzOND7Z52lQ1QSHVcEqixGP25aCz9+yueMiYhI90Rdpjto0CDcuHEDy5Ytw+bNm7F582YAgLm5OVJS/ncuniAImDZtGgtRIipd3p8ZRdk+C5nKv/SsXPx+NhL3nicrXVNViO6f0AaNahZ83BkREZG2iL7t65IlS9CxY0csWbIE586dQ25uLpKT3/4jqaenh7Zt22LGjBno3r272EMTEZUMl+lSGTNg3QXceab6nOz32VkYwaNWJc0mREREVAQaOYOkW7du6NatG9LS0hAeHo6UlBSYm5ujQYMG8p1qiYhKnfefWpBwZpRKr4S0LLULUQDYPbaVBrMhIiIqOo0eiGlqaorGjflcChGVEZwZpVLs6O0XOPPgFTJz3v6cBt6PVfte1+qWsLcx01RqRERExaLRYpSIqGx5f2aUxSiVDitPPMAvAeGFxk3s5Ah9qeLPbY1KxujZiGfREhFR6VPsYtTf3x8AYGVlhb59+yq0FYWvr29xUyAiEhc3MKJS6FVyhlqFaHUrY0zp6qyFjIiIiMRR7GJ0+PDhkEgkcHZ2lhejeW1FwWKUiFSKuQpEngZys7U35rPriq85M0qlwG+nItSK43mhRERU1hS7GPX19YVEIkH16tWV2oiISuThGWDrR4CQq9s8+PcZ6Vh0fBq2XIxSam/raAu3GpYAAIlEAo9aVujhXk3b6REREZVIsYvRvDNEC2sjIiqyO3t1X4gCgJ6hrjOgCuTSozjsuvwUiel5qwEEnLz7SmXsttEfai8xIiIiDeEGRkRU+mSn6TqDt+p31HUGVE7dfZ6E9Wcf4mVyBgAgOj4dUW/U+7kf2Ly2JlMjIiLSGhajRFT62TQAqrppbzwDE6BhH8ChnfbGpDLnaVwalh27j0evU4p0X3aOgPsvk4s9LjcpIiKi8kLUYvTAgQP48ssvMW7cOEydOjXfuOXLl2Pt2rX47bff0KNHDzFTIKLyyKUX0GW+rrMgAgAIgoD1QQ+x6PA9rY99fmYn2FkYaX1cIiIiTRC1GN22bRuioqLku+vmp0+fPpgxYwa2bdvGYpSIiMqMwPuvMHzTZY2P41zVAn0+qCF/Xc/WDF1cq0Jfjzs8ExFR+SFqMXr16lXY2tqiQYMGBcY5OzvDzs4Oly9r/h90IiKiksjIzsWNpwl4kZSBL3deF71/W3NDjO/oCACQSiRoUqcSPGpVEn0cIiKi0kbUYvTZs2dwd3dXK9be3h5hYWFiDk9ERCQaQRBw+NYLjN8eWmispbE+5vYu+nPNDaqao1FNKx6LRkREFZKoxaiRkRESExPVik1MTISenp6YwxMREYlmyt838O+1mELj1g5pih6NqhcaR0RERIpELUZdXFxw6dIlREREwNHRMd+48PBwhIeHw9PTU8zhiYiIiu3YnRc4cOMZ0rNyER2fXuiOt1UtjXBxpjekUs5qEhERFYeoxWi/fv0QEhKCkSNH4siRIzAzM1OKSUtLw6hRoyCRSPDRRx+JOTwREVGRPX6dir6/nUdierZa8XVtTNHBuQpm92rIQpSIiKgEJIIgCGJ1lpKSAg8PD0RFRaF27doYP348WrZsCSsrKyQmJuLixYtYu3YtoqKiULduXdy8eRPm5uZiDV+uuLm9ffbozp07Os6ESEQ5mcDBr4H7h9/+/3zjMgAh93+v23zFo11IY/r+dh43niYUGjetmzPGdajP5zuJiIhEIurMqLm5OQ4fPgwfHx88fPgQM2fOVIoRBAGOjo7Yv38/C1GiiubqZuD6Nl1nQQQAeJWcgdCoBLUK0W96uODz9vU1nxQREVEFImoxCrx9bvTmzZv4/fffsXfvXoSFhSEpKQmWlpZwd3dH//79MXr0aJiamoo9NBGVdq+KuYO2BTeHIXFtufAYc/fnv/Kkl0d1uNWwhL5UgmZ1K6NpHWstZkdERFQxiLpMl8TDZbpULh348u3saFHYuQDDDwNmNhpJiSqeCxGvMfjPkHyvX5rtjSoWxlrMiIiIqGISfWaUiEhtrv2A1hPzv25g8rYYlfIYKCq5rBwZVhy/jz/OPsw3xsRAj4UoERGRlrAYJSLdMa8K1Gqm6yyonLv8OA5jt17Fm9SsAuP6fVADc3u7aSkrIiIi0kgxGhAQgEOHDiEyMhIpKSnIbyWwRCJBQECAJlIgIiICAHyy7mKB17u4VsV6X34oQkREpG2iFqOZmZn49NNPcfDgQQDItwjNw+3xiYhIE2QyAYduPcc/V6MLjKthZYwf+rlrKSsiIiJ6l6jF6IIFC3DgwAGYmZlh1KhRaNmyJapWrQqpVCrmMERERAXy23QJQeGvC4z5ZVATdHC2g6WxgZayIiIioneJWozu3LkTUqkUhw8fhpeXl5hdExERFehlUgZGb7mCWzGJBcadm9ERNSuZcHUOERGRjolajMbExMDBwYGFKBEBWWlA6ivFtsxk3eRC5V74y2R0WXW20LiZPVxQy5rnXBMREZUGohajdnZ2sLS0FLNLIiqLLvwKBCwAcgvevZRIDM8S0gstRD9qUhPeDavAx6OGlrIiIiKiwohajPr4+GDTpk2Ii4tD5cqVxeyaiMqKzGTg5DxAlqPrTKgCCHuWhJ6/BBUYs+KTxhjgWUtLGREREZG6RN1ZaP78+bC1tcXIkSORmpoqZtdEVFYkv1S/ELWpr9lcqFy7HZNYYCG6Y0xLXJvThYUoERFRKSXqzOjRo0cxduxYLFy4EI6Ojhg0aBAaNGgAMzOzfO/x9fUVMwUiKgskeoBzD6DJUF1nQmVMRnYuTt59iQnbr+UbY6gnxf3vu3ODIiIiolJOIhR2GGgRSKVSSCQS+fmi6vwikJubK9bw5YqbmxsA4M6dOzrOhKiIXkcAqz0V22Y+AaTvHJ8h1Qf0DbWbF5VpgiBg99VoTNt9s8A4U0M97B3XBs7VLLSUGRERERWXqDOj7dq14yfRRKTMwBTQ41mOVDxP49Lgtey0WrGHJ3mhrm3+q3GIiIio9BC1GA0MDBSzOyIiquDSs3LVKkR7eVTHLwObQE/KD0SJiIjKClGLUSIiIrFEvEpG55UFH9nyeft6mNLFGYb6ou7HR0RERFrAYpSIiEqdQzefY/z20Hyvz+vtCt9WdSHlTCgREVGZxWKUiIhKjdAn8Zj17y3ce5Gcb8zt+d1gbsR/voiIiMq6Yv9rrqenBwBwcXGR7/ia16YuiUSCnBw1zyMkotLhSTBw8TcgNVb19ew07eZD5cLlx3G4FZ2IBQfDCow7PrkdC1EiIqJyotj/oucd3yKTyZTaitoHEZURGYnA1o9YcJKoBv0RjIsP3xQY42Brhh8+codTVR7ZQkREVF4Uuxh9twgtqI2IypEXt4peiBqYAZKirZqgikEmE/D72YeFFqKj2zrgWx9XLWVFRERE2sK1TkSkPllu0e/xHA5IudMp/c/N6AQcuvUcv595WGjs9e+6oJKpoRayIiIiIm1jMUpExWdoAXSZn/912waAfVvt5UOl3ri/ruLwrRcFxnjaW8O5mgUmdnJkIUpERFSOsRglouIzMAGaj9J1FlQGnI94jSF/hhQaN6GjI6Z2c9ZCRkRERKRrXDtHREQalZGdq1Yh6tvKHl93cdJCRkRERFQacGaUiIg0Ii0rB4sO38W24Cf5xnjaW2PIh3XQv2ktLWZGREREpQGLUSIiEo1MJuBmTCK+2HYVzxMzCozd80VreNpbaykzIiIiKm1YjBJVFDIZIBRjN1yFPnLEyYXKrS6rziAyNrXQuMhFPaEnlWghIyIiIiqtWIwSVQQXVgNBK4D0eF1nQuXYvusxahWiYQu6sRAlIiIiFqNE5V5iNHB8tq6zoHIsLSsHq048wPqgRwXGrRnSFO2d7GBqyH96iIiISIPFaFZWFkJDQxEdHY20tDT4+vpqaigiKsibCM31bcVNZyq6V0kZaLEoIN/rHZztsOijRqhRyUSLWREREVFZIPrRLjk5Ofjuu+9QtWpVtGnTBp999hlGjBihEPOf//wH9erVQ0SEBn9JJiLNMrIEvL/TdRakQ4IgFFiIfufjis0jWrAQJSIiIpVEnRnNzc1F7969cfz4cQBA3bp18fr1a6SkpCjE9ejRA3/++Sf27t2LadOmiZkCEaljYmjJ+6hUB9AzKHk/VGYtPHg332sdne0wok1d7SVDREREZY6oxegff/yBY8eOwcXFBTt37oSHhwe8vLxw4cIFhbju3btDT08Phw8fZjFKpG1mVQCb+rrOgsqo2ORMTNwRiuCHcfnGbB/9IVo72moxKyIiIiqLRC1Gt2zZAqlUil27dqFRo0b5xpmYmKB+/foICwsTc3giItKgjeceYcHBgv/efrioJ6TcKZeIiIjUIOozo3fv3oW9vX2BhWgea2trJCQkiDk8ERFpSEJaVqGF6L7xbViIEhERkdpEnRnNycmBhYWFWrEpKSkwMeGmFlTBpLwCbuwEUl5qb8yEJ9obi8ql2ORMNP/hZIExfRrXQOPalbSTEBEREZULohajtWvXRkREBHJycqCvn3/XCQkJuH//Pjw8PMQcnqh0y80BNvXQ7FErRCUgCAJeJmUiK0f29jUE/HoqAruvRquMtzU3wqrPGsO5mgWqWBhrM1UiIiIqB0QtRrt06YI1a9bgl19+wddff51v3OLFi5Gbm4uePXuKOTxR6fYqrHQUovpGus6ASplLj+JwIuwF1gc9KtJ9V77trKGMiIiIqCIQtRidNm0aNm/ejJkzZyIlJQVjxoxRuB4VFYUff/wRq1evhrW1NSZOnCjm8ESlW06GrjN4y6m7rjOgUuSnkw/w08nwIt93bU4XDWRDREREFYlEEARBzA4PHjyIgQMHIj09HQAglUohk8lgamqKtLQ0CIIAMzMz/Pe//4W3t7eYQ5crbm5uAIA7d+7oOBMSzdNLwIZ3foGX6gMeA7U3vgRA1UZAs5GAvqH2xqVSSyYTUG/W4SLdY6gvxX/HtYFrDUsNZUVEREQVhagzowDg4+ODK1euYO7cuTh48KC8KE1NTYWhoSF69eqF77//Hg0bNhR7aKKyRd8E6PebrrOgCuzI7RdFit87rjWa1LHWUDZERERU0YhejAKAi4sLdu3ahezsbISHhyMhIQHm5uZo0KABd9AlItKR8JfJOPMgFpn/v0HR8mP3lWIa166EYS3t4eNRXd4mlUhgqC/qSWBEREREmilG8xgYGMDV1VWTQ8gFBgZi/vz5uHLlCvT09NCqVSssWrQITZo0UbuP33//Hb/99hvCw8NhZWUFHx8fLFq0CFWqVFGICwoKwp49exAYGIjHjx8jKysLzs7OGDp0KCZNmgQDAwOx3x4RUZGlZeVg//VnWHsmElFv0tS6Z9/4NhrOioiIiOgtUT/q/uSTT7B//37k5OSI2W2hDh06BG9vb9y4cQN+fn4YOnQoQkJC0KZNG1y+fFmtPmbOnImxY8ciMzMTEydORKdOnbB582a0adMG8fHxCrGffPIJ1qxZgypVqmDMmDEYOXIk4uPjMXXqVPTq1QsymUwTb5PKuuc3dJ0BVSB/X3kK1++OYea/t9QuRI0NOPtJRERE2iPqBkZSqRQSiQTW1tb47LPPMGTIELRu3Vqs7lXKyspC/fr1ERcXh9DQUDg7OwMA7t27B09PT7i4uODq1asF9nH79m00btwYbm5uCA4OhqmpKQDA398ffn5+mDx5MlauXCmPX758Ofz8/BRmTNPT09GxY0eEhIRgx44dGDiwZBvTcAOjcujYbODiasW2eYm6yYXKvbozDxX5nt8GN0Wvd5bnEhEREWmSqB+Df/vtt3BwcEBcXBzWrl0LLy8vODo6Yt68eQgPL/rRAeo4ceIEoqOjMWzYMHkhCrx9bnXIkCEIDQ3FtWvXCuxjy5YtkMlkmDVrlrwQBQBfX184OTnB399fYbZ32rRpSkt3TUxM8NVXXwEAzp07J8I7o3LHyELXGVA59TQuDcuO3sPUf25g6j834LvxUqH31LMzQ+eGVdG5YVX4eFTH2iEsRImIiEi7RH1mdMGCBViwYAEuXryIbdu24Z9//sHDhw+xcOFCLFy4EM2bN8ewYcPw2WefwdbWVpQxg4KCAEDlMTGdO3fG+vXrERQUVOCzowX14e3tjbVr1yIsLAweHh4F5pL3rKi+vvpf1rwZ0PdFRkaifv36avdDZZBzL11nQGVYrkzA2fBYjNik3qMIAODXyh6tHW3RvG5lVDbj8T5ERESkWxrZwKhVq1Zo1aoVfv75Zxw9ehTbtm3DgQMHcOnSJVy+fBlff/01unTpIi9MSyIiIgIAVBZueW15MQX1YWFhATs7uwL7KKwY9ff3BwB06tSp8MSJpHq6zoDKkNjkTDyJS8XPARHIzM5FyKO4It3/eAk//CAiIqLSRaO76err68PHxwc+Pj5ISUnB7t27sX37dpw6dQqHDx/G0aNHS1yMJiUlAQAsLZUPYM9rS0ws+Lm8pKQkpWW3Re3jr7/+wv79+9G2bVv07t270Lzz5PdMaH4zpkRUsaRk5mDMliu4+PBNsfsY1KK2iBkRERERiUOjxei7zM3NMXz4cNjb20NfXx9Hjx6FiHsn6VRQUBDGjBmDGjVqYPv27ZBIJLpOiUoLQQAu/wk8DARe3dV1NlSGyGQCjt55gXF/hRbpPnsbU3R1rSp/3aCKBT5qWlPs9IiIiIhKTCvF6K1bt7Bt2zbs2LEDMTEx8vainAGan7yZy7wZ0nfltVlZWRXah6r71enj8uXL8PHxgbm5OU6ePInatTkDQe+4/CdweKqus6Ay5nliOlotPlWkez5tVgvz+7jDxJDLv4mIiKhs0FgxGh0dje3bt+Ovv/7C7du3AQCCIKBOnToYPHgwhg4dCldX1xKP4+joCODthj9NmzZVuBYZGakQU1AfISEhiI2NVXputKA+rl27hm7dukFPTw8nTpxAw4YNi/0+qJx6dDb/a9xdl94jCAK6rDqLiFcphcY2qmmFAZ61UNfWDG0dbaEn5YoMIiIiKltELUaTkpLwzz//YNu2bQgKCoIgCBAEAVZWVhgwYACGDh2K9u3bizkkvLy8sHTpUgQEBOCTTz5RuHby5El5TGF9hISEICAgQOl80ICAANjY2CgVznfu3EHXrl2Rk5ODkydPonHjxiK8Gyp3ZLmq2yV6QKMB2s2FSrXbMYnw+bXgY6GkEuD45PaoZ2sGKYtPIiIiKuMkgogPbpqamiIzMxOCIMDAwAA9evTA0KFD0bt3bxgZGYk1jILMzEw4OjoiLi4OoaGh8rNG7927B09PTzg7OyM09O0zV9nZ2YiMjISpqSnq1Kkj7+P27dto3Lgx3NzcEBwcLD9r1N/fH35+fpg8eTJWrlwpjw8PD0e7du2QlJSEo0ePFlrsFkfeBkb5bXBEZcSOwcD9Q/97bWgBdJgJOLQDqhe8OzNVHM8S0tF6ScHLcsd1qI9J3g1gbMBluERERFQ+iFqMSqVStGrVCkOHDsVnn32GypUri9V1gQ4ePIi+ffvCysoKgwcPBgBs374d6enpOHPmDFq0aAEAePz4MRwcHNC+fXsEBgYq9DFjxgwsW7YMTk5O6Nu3L2JiYrBr1y44ODjg0qVLsLa2lsfa29vjyZMn8Pb2Rtu2bZXy+eCDD9CvX78SvScWo+XE+8Vop2+BdtN0lw+VSnVnHsr3WjsnO2we3pwzoURERFTuiLpMNzIyEg4ODmJ2qRYfHx+cPHkS8+fPx+bNmyGVStGmTRv88MMPSs+R5mfJkiWoV68eVq9ejV9++QVWVlbw8/PD4sWLFQpRAHjy5AmAt0t4AwIClPry8/MrcTFKROXf1ah4fLz2Qr7Xb83rCgtjAy1mRERERKQ9os6Mkng4M1oOyHKBBe+tDuDMaIX1MikDzxMzsOrEAyRnZCP0SUKB8Q8X9eRsKBEREZVrWjtnlKjCubFT1xmQjj1LSMfW4CisDYws0n1B0zuyECUiIqJyr9jFaL169QC8PfLk+PHjCm3qkkgk8qNTiMqdlypmtc2raj8P0rr41CyM9r+Cq1HxRb53QV831K5sqoGsiIiIiEqXYhejjx8/BgAYGxsrtalLIuEn/1SeqVgB7+Kj/TRIq86Fv8bQDSFFvm9Qi9qY4+MKU0MuWCEiIqKKodi/9Tx69AgAYGBgoNRGRADefxz7gyGAqXZ2mCbtkskEhD1PwtR/buDei2S17qluZYxxHerD0sQAHZyqwMqUGxURERFRxVLsYtTe3l6tNiL6f1KeD1kehTx8g8/+CC40rk5lU0zp6oQ+jWtwVQgRERERRN7A6OzZs7CyskLjxo0Ljb158yYSEhLQrl07MVMgKrq0OCBTvdmsItFEn6QzyRnZOHDjOWIS0uRtb1KysPPy00LvvTTLG1UsjQuNIyIiIqpIRC1GO3ToAC8vL5w5c6bQ2C+//BJBQUHIyckRMwUi9eVkArtHAvcO6joTKuXepGTC8/uTRb5veOu6mNfHTQMZEREREZV9ou+UUZRjS3nEKelUxEkWolSg6Pg09PgpCMmZRfvQzLGKOdYN9YRjFXMNZUZERERU9uls28Y3b94o7MRLpHUJhS+vFFXl+todj4olKSMbR2+/wMKDYUjOKFoR6mBrhtNTO2gmMSIiIqJypkTFaFJSEhISEhTaMjMz8eTJk3zvSU9Px5kzZ3D79m24urqWZHiiskEiBep7A81G6joT+n+ZObl48CIFb1Iz8XNAOIz0pZBKJLgVk6h2AdqncQ35/xcAtHW0wafNamsoYyIiIqLyp0TF6KpVq7BgwQKFtitXrsDBwUGt+wcPHlyS4YnEVas54Ltf/H6l+oC+ofj9UpGlZ+ViwcEw7LiU/wdmhalkaoDr33UVMSsiIiKiiqlExaggCArPfUokkkKfAzUxMUH9+vUxaNAgTJ8+vSTDExVPbjbw8g6Q8F5BYmACGJrqJifSuKO3n2PsttBi329prI+949ugnq2ZiFkRERERVVwSQcRdhKRSKdq2bYuzZ8+K1WWF5eb2dgfOO3fu6DiTcibuEbC5F5AUo3zNoR3gd0D7OZFGZOfKsDYwEvdfJCMoPBZJRXz+M8+nzWrh8/b1Ud+OmxERERERiUnUDYzmzp2LOnXqiNklkbiublJdiFK58tvpCCw/dr9I9zSpUwl9//85UDMjfbR3suPZoEREREQaJHoxSlSqJb/M/1oVngdZHrxMylC7EN3zRWt42ltrOCMiIiIiUkVnR7sQlRrGlQD7NkCHGbrOhEroalQ8Pl57odC4+X3c4Ne6ruYTIiIiIqJ8aaQYjY6Oxo4dO3Dt2jW8efMG2dnZKuMkEgkCAgI0kQKRetp8CXRZUHgclVoZ2bkIf5mCgX9cRGpWbr5xY7wcUNXSGP2a1IStuZEWMyQiIiIiVUQvRtesWYOvv/4aWVlZkEgkAKC0425eW97/J9Ko5zeA6CuAIAPehOs6GxLBq6QMhD6JV2t3XFNDPYQt6K6FrIiIiIioKEQtRk+fPo2JEyfCzs4OP/zwA37++WfcuXMHAQEBePPmDYKDg7F582akp6dj2bJlcHd3F3N4ImU3/wH+Ha3rLEgE6Vm5mLHnJvbfeFak+3b+p6WGMiIiIiKikhD1aJd+/frhwIEDCAgIQIcOHeDl5YULFy4gN/d/S+diY2Ph4+ODBw8eIDQ0FA4ODmINX67waBeRbPYBHgflf73t10BnbrxVmr1IzMDMf28i8H5ske7r4V4NU7o6w7EKj2QhIiIiKo1EnRkNCQlBlSpV0KFDh3xj7OzssHPnTjRo0AALFy7Exo0bxUyBSFFGYsHX7VtrJw8qVGJaNiJikwEAmTkyrDvzEGcfFK0ABQC3GpZYN9QTtSubip0iEREREYlI1GI0Li4OHh4e8teGhoYAgNTUVJiZmcnbHRwc4ObmhpMnT4o5PJF67NsABiaAaz+gQRddZ1MhxSSkY3tIFA7dfI4qlsa49CiuRP35tbLHRO8GsDEz5LPoRERERGWEqMWojY0NMjIy5K9tbW0BAI8ePVJ6PjQ3NxevXr0Sc3iiwn30O9B4oK6zqLAEQYDDN4cV2h6/SStWX39/3goetaxgbKAnRmpEREREpGWiFqO1a9dGZGSk/PUHH3yA3bt3Y+/evQrFaHh4OB48eAA7Ozsxh6eK5kkwEOoPpCfkHxP/WFvZ0DsEQcC5iNe4/yIZmTkyLD92H8YGUmRky0rUb10bUyzu74FW9W1EypSIiIiIdEXUYrRjx464cuUKIiIi4OjoiEGDBmHevHlYsGABUlNT4eXlhefPn2PRokXIzc1Fnz59xByeKpKEp4B/XyAno/BY0qjT91/hYuQbSCRA+MsUnLqnesVDUQpRM0M9SCUSJGfmoLqVMb7p2RBdGlaFiSFnQYmIiIjKC1GL0f79+2PPnj24ePEiHB0dUbduXSxduhRff/01li9fjuXLlwN4O2vi7OyM77//XszhqSJ5GlK8QtTQrPAYKtSt6ESsOvkg38KzqCZ2ckQVCyPYWRihnZMdTA1FPwKZiIiIiEoZUX/ja9GiBcLDwxXavvrqK3z44Yfw9/fHo0ePYGpqinbt2mHMmDEKmxoRFUludtHvsawJ1G0rfi4VzLKj97AmMLLwQDVcm9MF1maGovRFRERERGWLVqYfWrVqhVatWmljKKqozKsBLUbnf93EGnDxefu/pBZBEJCVK8Pj12nwv/gYf4U8KXZfXg1sEZeaBY9aVujZqDosjA3gXsMS+npSETMmIiIiorKEa+Go9MjJBJ7fBHIzC499/UDxtUVVoN00zeRVAaRm5uBp/Ntdba89ScA3/94qVj+dG1aFpYk+0rNy0adxDTR3qAxbcyMxUyUiIiKicoLFKJUOcY+ATT2A5Oe6zqRCkMkELDgYhjMPYvHodWqJ+urkUgUfOlRG3w9qopqVsUgZEhEREVF5V+xitFOnTiUeXCKRICAgoMT9UDkQuoWFqBbkygRsD4nCnH13ROlv3VBPdHevJkpfRERERFSxFLsYDQwMLPHgEomkxH1QOZESW7L7q7oXHlOBPY1Lw+gtV3D/ZXKJ+rGzMMIX7eujcW0ruNe0gpE+j1ohIiIiouIpdjF6+vRpMfMgUmZSufAYiRSo3QLoskDz+ZQxT+PScOzOC0S8SsHOy0/Vvk8iAQTh7f//xLMWRnvVg4WxPqpbGfMDJCIiIiISTbGL0fbt24uZB1V0aW8UX7eaAHT7QTe5lGGCIOD3sw+x5Mi9It03r7cralmbom0DWxgbcLaTiIiIiDSPGxiR7h39BnhwRNdZlAsO3xwuUrxvK3vM6tmQBSgRERERaR2LUdKtxBggeI2usyjzlh+7h99OR6odf+RLLzSsbqnBjIiIiIiICiZqMbpgQdGe25NIJJgzZ46YKVBZk/RMdbutk3bzKMM6LD+Nx2/SCoxxqWYBYwM9jG1fn7vfEhEREVGpIBGEvK1KSk4qlUIikUBVl+9vfCIIAiQSCXJzc8Uavlxxc3MDANy5I84RHKXW08vAhs6KbR4DgT6/APpGusmpDBAEAQ9epqDbT2cLjJvSxQm+revCysRAS5kREREREalH1JnRuXPn5nstNTUVDx48wLFjxyAIAsaPHw9LSy4TrNAEAUh+b2bU0ALo/7tu8inlsnJk+PvKUyw6fBdpWYV/iLN1VAt4NbDTQmZEREREREUn6syoOh4+fIjPPvsM2dnZuHjxIkxMTLQ5fJlR7mdGk54B2z4GXoUpthtaALOidZNTKRWfmoWRWy7j2pMEteI3+DVDJ5cqPIaFiIiIiEo1rW9gVK9ePezcuRNOTk5YuHAhFi1apO0UqDS49IdyIUpymTm5cP3uGHJlRfusaP+ENvCoVUkzSRERERERiUiqi0Hr16+Phg0b4u+//9bF8FQaJOYz+2lTT7t5lEJvUjLh/O3RIhWidhZGuP99dxaiRERERFRm6OxoF6lUiuhoLsekd1jXBXqu0HUWOhXxKhmdVxa8KVGerq5V8W0vV9SxMdVwVkRERERE4tNJMRoVFYX79+/DxsZGF8OTNgkCkBAF5GQptmckKb5uOR7oXnGXbCekZSH8VQo+WXex0FguxSUiIiKi8kCrxeirV69w8eJFzJ49Gzk5OejZs6c2hydtS34B+PcDYu8WHmtgrPF0SqO0rBy4fnes0Lg9X7SGo505rEx5RAsRERERlQ+iFqN6enpqxQmCAHt7eyxcuFDM4am0ubJJvUK0AnqemI51gZHYcjGq0NjIRT2hJ+XOuERERERUvohajBZ2SoyZmRkaNGgAHx8fTJkyBVZWVmIOT6VNUhGeCbZpoLk8ShFBEPDNv7ew8/JTteJvzO3KQpSIiIiIyiVRi1GZTCZmd1QR6BsDbh8B7h/rOhONuxDxGoP/DFEr1qmqOY591Y5nhRIRERFRuaWz3XSpnJPlAlEXFNs+HAt0e3+TIgkg1ckJQ1oTHZ+G7w/exdE7LwqNvf99dxhIpZByNpSIiIiIyjkWo6QZh6YAcQ8V2yR6gFS954rLA0EQ4PDNYbVit4xsgfZOdhrOiIiIiIio9GAxSuLLTgeubVNuL+czoAAQn5qFJgtPwMxQD6lZuYXGbxzeDJ1cqmohMyIiIiKi0kX0YjQ+Ph4//vgjDh8+jMjISKSkpOQbK5FIkJOTI3YKpGvZ6YAsW7m9Vgvt56Ilc/fdVtgZt7BCdGDz2ljysYem0yIiIiIiKrVELUYfPXqEdu3a4dmzZ4XurAsUvvsulSNeU4CGvXWdhai++fcmLj2KQ2Rsqtr3uFSzwGivehjgWUuDmRERERERlX6iFqPTpk1DTEwM6tati+nTp6Np06aoUqUKdwStCB6fB27sADKTgdws5esfjgXKwc9BckY2tgU/wdKj94p874Pve8BQv/wvVSYiIiIiUoeoxWhAQACMjIwQGBiIOnXqiNk1lWaxDwD/vqqX5pYjcalZaLrwRJHu2TKyBRpWt0AVC2MNZUVEREREVDaJfs6oi4sLC9GKJupcIYWo5O15omVYrkxQuxBdPbgJ2jnZwdLYQMNZERERERGVXaIWo25ubnj58qWYXVJZkFvIJlRO3QFjS+3kIpLMnFwE3o/F6lMRuBWTWGj8dz6ucK1hiZb1bLSQHRERERFR2SdqMTphwgQMHToUJ06cQJcuXcTsmkqj7HQgNxvISVe+1n7m2/+1tgdc+2k1rZJIz8rF94fC8FfIE7Xip3VzxtCW9rAy4SwoEREREVFRiFqMDh48GKGhofjkk08wf/58jBw5EhYWFmIOQaVBZgqwZxQQfhwQZMrX63oBHb/Rfl4lkJUjw+dbr+D0/Vi17+GGRERERERExSf6OaOLFy/G48eP8fXXX+Prr7+Gra0tzMzMVMZKJBJERkaKnQJp2vW/gAdHdZ2FKHJlAkZtuYzAIhShAHB6agcWokREREREJSBqMRoXF4cuXbrg+vXr8jNEY2NjERur+hd9HvlSRsU9Kvh6JXvt5FECD14mY/mx+zgRpt4zzk5VzTGugyM6NazCjYmIiIiIiEQgajE6a9YsXLt2DZUrV8aYMWPQtGlT2NnZseisSCrXA7y+1nUWBfrnylNM231Trdi/Rn+INo62Gs6IiIiIiKjiEbUYPXDgAPT19XH69Gk0atRIzK6pVBEUXzp2Bnr/Akj1AfMqQCn98OHG0wT0/e28WrEHJ7aFe00rDWdERERERFRxifrQW0JCApycnHRSiAYGBqJjx46wsLBApUqV0KNHD1y7dq1Iffz+++/w8PCAiYkJqlWrhtGjR+PVq1dKcZGRkfjuu+/Qo0cP2NraQiKRYODAgWK9ldIvZJ3ia8sagFVNwKJqqSxEkzKy8cW2q2oVote/64LHS3qxECUiIiIi0jBRZ0br16+PnJxCzpzUgEOHDqFPnz6wsrKCn58fAGD79u1o06YNzpw5g+bNmxfax8yZM7F06VI4OTlh4sSJiI6OxubNm3HmzBlcunQJ1tbW8tigoCAsXLgQhoaGcHR0xJs3bzT23koly5pAUsz/Xqe+1l0u+ZDJBLxOzUSLHwIKjTXQk2DFJ43R26MGpNLSV0wTEREREZVHohajI0eOxJQpU3Dt2jU0adJEzK7zlZWVhbFjx8LY2BgXL16Es7MzgLdnnnp6emLs2LG4evVqgX3cvn0by5cvR6NGjRAcHAxTU1MAQPfu3eHn54eFCxdi5cqV8vj27dsjJCQEjRs3xvPnz+Hg4KC5N1gamVdRLEYNTHWXy3syc3LRd/V53HuRrFb8tG7OGN/RUcNZERERERHR+0Rdpvvll1/i008/RZ8+fbBv3z4xu87XiRMnEB0djWHDhskLUQBwcXHBkCFDEBoaWuhy3S1btkAmk2HWrFnyQhQAfH194eTkBH9/f4UZXwcHB7Ro0QJGRkbiv6GyqH4nXWcAAEjLyoHzt0fVKkR9W9kj/IceLESJiIiIiHRE1JlRb29vAMCrV6/Qv39/WFtbo379+gWeMxoQUPgyyoIEBQUpjP2uzp07Y/369QgKCipwpragPry9vbF27VqEhYXBw8OjRLmWG8+K9iyupt2KTkTv1efUinWsYo7F/Ruhed3KGs6KiIiIiIgKImoxGhgYqPA6Li4OcXFx+caLceRLREQEgLfPq74vry0vpqA+LCwsYGdnV2AfmihG3dzcVLZHRkaqfE+lk1B4iAa8Ss5Q65lQADDUl+LGd11hYqin4ayIiIiIiEgdohajmzZtErM7tSQlJQEALC0tla7ltSUmJhbaR5UqVVReU7ePikUChQJUlqv1DC4/jsMn6y4WGnduRkfUrGTCs26JiIiIiEoZUYvRvJ1sSX137txR2Z7fjGmpVNNTK8PkygRM2nkNh24+Vyv+7oLunAklIiIiIiqlRC1GdSFv5jJvhvRdeW1WVgWfGWlpaany/qL0UbG8tyxXIuo+WMqjCQJ+OHQXf557VGisVwNb/OnXDEb6LEKJiIiIiEqzMl+MOjq+3Q01MjISTZs2VbgWGRmpEFNQHyEhIYiNjVV6blTdPiqM7AytDJOckY1Hr1Nx9kEsVhx/UGi8uZE+LnzTCZbGBlrIjoiIiIiISkrUYtTf37/I9/j6+pZoTC8vLyxduhQBAQH45JNPFK6dPHlSHlNYHyEhIQgICMDAgQMVrgUEBMDGxgaurq4lyrPceKliWbFhyc4ZPX7nBfaERqOymSF2Xn4KoYj7IfVwr4a1Q7WzVJiIiIiIiMQhEYSi/uqfP6lUqvZGMYIgQCKRIDe3ZJvfZGZmwtHREXFxcQgNDZWfNXrv3j14enrC2dkZoaGhAIDs7GxERkbC1NQUderUkfdx+/ZtNG7cGG5ubggODpafNerv7w8/Pz9MnjwZK1euVDn+48eP4eDggM8++ww7d+4s0Xt5V94zo/k9U6ozT4KBjd0U2+YmAMXcIMhp9hFk5cqKde+83q74tHltmBqW+Ql+IiIiIqIKR9Tf4n19ffMtRlNTU/HgwQPcvHkThoaGGDBgAAwMSr6k0sjICGvXrkXfvn3RqlUrDB48GACwfft2yGQyrFu3Th4bExODhg0bon379grH0Li7u2Pq1KlYtmwZmjRpgr59+yImJga7du2Co6Mj5syZozDm69evMXXqVABASkoKACAkJATDhw8HALi4uGDmzJklfm9lRjEK0d1XozH1nxvFGu7Il15oWF1592QiIiIiIio7RJ0ZVcf58+fh5+eH+vXr48iRI5BKxdn85vTp05g/fz6uXLkCqVSKNm3a4IcfflB4jjRvFvP9YhR4O1P7xx9/YPXq1QgPD4eVlRV8fHywePFipWNf8vrJj6r+i6rUzozumwBc2/q/10ZWwDdP1Lo1VyZg3/UYfP130YpQI30pMnNkmNbNGeM78tldIiIiIqLyQOvFKABcu3YNnp6eWLJkCaZPn67t4cuEUluMzntvV2FDC2BWdIG3pGXlwPW7Y2p1P8CzFoz0pcjOlWFU23pwqmrOM0KJiIiIiMohnRSjAODs7Ax9ff3SV2yVEqW2GF1gC8iy//fa0ByYFZNveFaODE7fHlGr63VDm6K7e/WSZkj0f+3deXhUVbY+/rcqc2U2kARJAgkhIWQCIgEJGCCMgtDQGA2g0AwX+oeAzDZeRq+gIrSttKIokw1eW9RuxsscDQShhRDmIZAgggIyZCYkVev7B786UlRVqkJCKhXez/PkeTj7rLPPOpV9tFbOsImIiIjIDtjszS8ajQZnzpyx1e6ppjz3N7OrtDqxqhCd0DUcf0oKha+7c01mRkREREREdZhNitGbN2/izJkzcHd3t8XuqTruvyoKAA0iTIaJCJrN3FJpV38f3AZ94ngllIiIiIjocVTrxWh2djYmTpyIsrIyPPfcc7W9e6qO4t+sDg39i/lC9D+vd0NDT5eayIiIiIiIiOxUjRajYWFhZteJCK5du4Y7d+5ARODr64s33nijJndPj9ovJt6C6+ZrsGjpGdFzb/aGk0PNvEGZiIiIiIjsV40Wo3l5eRZjfH190adPH8ybN6/S6VGoDjL1riufYOWfOgvPiO6c/AwLUSIiIiIiAlDDxWhubq7ZdSqVCu7u7vDz86vJXZItuf8+/6pI5YVo7sJnOUULEREREREparQYbdKkSU12R3VNyQPPjDo4Kf/8w9/3oUJnepago3N7sBAlIiIiIiIDNpvahezQjfOGy3eLAQC5vxUj++d8k5scm9sDnq5OJtcREREREdHjq9oP8L377rsYOHAgPvnkE6viP/74YwwcOBDvvfdedXdNtc3D33D5zm1odYIu76abDN85OZmFKBERERERmaQSMfVWGutcuHABLVq0QMOGDXHy5El4e3tb3Ob27duIjo7GjRs3cO7cOQQHB1vc5nEUHR0NADhx4oSNM7nPweXAlqm/LzeKR9PcGSZDM6Z3QfATmlpKjIiIiIiI7E21royuXr0aWq0Wf/nLX6wqRAHAx8cHM2fOxN27d7Fq1arq7J5qW1mBwWKBztVk2MiOoSxEiYiIiIioUtUqRnfv3g21Wo20tLQqbTd48GA4ODhgx44d1dk91bZj6w0WT/5SYDJsVt+WtZENERERERHZsWoVo6dPn0ZoaGiVp2vx9fVFaGgoTp8+XZ3dU21r1Mpgsb36lFHIgZkptZQMERERERHZs2oVowUFBQ89b+gTTzyB/HzTb2ClOuqB6Vl2a1sZLP8pqSkCvEzfuktERERERHS/ahWjnp6euH379kNtm5+fDw8Pj+rsnmrbNcMroTnS2GB5znPRtZkNERERERHZsWoVo8HBwcjJyalyQXr79m3k5OQgJCSkOrun2nSnALhy2OzqzRM61mIyRERERERk76pVjHbu3Bk6nQ6fffZZlbb79NNPodVq0blz5+rsnmrTlSyjplK4KP+OftK6tykTEREREREB1SxGR40aBQCYO3cu/vOf/1i1zcGDBzFv3jyo1WqMHDmyOrun2qQtN2rark0AADRr6F7b2RARERERkZ2rVjEaHR2N0aNHo7i4GMnJyViwYAFu3rxpMvbmzZt488030blzZ5SUlGDkyJGIiYmpzu7Jxk5IKABg5fBEG2dCRERERET2RiUiUp0OysvL8dxzz2H79u1QqVRwcHBAdHQ0wsLC4OHhgaKiIuTm5uL48ePQarUQEXTv3h2bNm2Ck5NTTR1HvRMdfe9lQCdOnLBxJv+/rLXAv/8/ZfGKPIEOZUsBAHlv9bFVVkREREREZKccq9uBk5MTtm7digULFmDx4sW4ffs2srOzkZ2dDZVKhftrXW9vb0yZMgUzZ86EWl2ti7JU2y4dMFh0hM5GiRARERERUX1Q7WIUAFQqFV5//XVMnDgRW7Zswd69e3H58mUUFhbC09MTjRs3RseOHfHss89yOhd75RFgsOivug0AiAzwtEEyRERERERk72qkGNXz8PBAamoqUlNTa7JbqgvE8Erofm1LAMA/xz5ti2yIiIiIiMjO8V5Zss4DxehV+AAAvN343C8REREREVUdi1GyzgPFqI5Dh4iIiIiIqoEVBVnngWJUoEKLQD4vSkRERERED4fFKFnnwSujokJCE18bJUNERERERPaOxShZRafTGi5DjQ7NGtgoGyIiIiIisncsRskq5cf+ZbCsA6+MEhERERHRw2MxSla56NzMYDlQdROB3q42yoaIiIiIiOwdi1GyitrVy2BZoyqzUSZERERERFQfsBglq/xyu8RgOVsVZaNMiIiIiIioPmAxSlYpLSs3WA700dgoEyIiIiIiqg9YjJJV3BwNl0vLxTaJEBERERFRvcBilKxSdOeuwbKXxsVGmRARERERUX3AYpSsoobhldCQBh42yoSIiIiIiOoDFqNkkYggRp1r0KbVqWyUDRERERER1QcsRsmicq2gseqGQVsDLzcbZUNERERERPUBi1GyqFyrww3xNGjz1N6yUTZERERERFQfsBgli4rvVkBgeFtuRcgzNsqGiIiIiIjqAxajZFGFVtBAVWDQ5qJxt1E2RERERERUH7AYJYsqKnRGbY5qDh0iIiIiInp4rCjIIim6atSmdnSyQSZERERERFRfsBgli7TacqM2h4AoG2RCRERERET1BYtRsii/xLgYVakdbZAJERERERHVFyxGySJntfEzo0RERERERNXBYpQscrqVY9yodqj9RIiIiIiIqN5gMUoWVejEuNGZU7sQEREREdHDYzFKFmm1vE2XiIiIiIhqFotRsui3gmKD5SuqABtlQkRERERE9QWLUbLI283wzbl3tTZKhIiIiIiI6g0Wo2SR7oHbdHVQ2SgTIiIiIiKqL1iMkkUihsWokyPfpEtERERERNXDYpQsEp3hfbk6DhsiIiIiIqomVhVkkfetYwbLouKwISIiIiKi6mFVQRaVOjcwWG6qvWijTIiIiIiIqL5gMUoWaVV8RpSIiIiIiGoWi1Gy6EbhHYPlY87xNsqEiIiIiIjqCxajZJGzo+EwuasVG2VCRERERET1BYtRssjVyXCYlN7VmokkIiIiIiKyDotRskh0hldCfTTONsqEiIiIiIjqCxajZJHIA7flqlS2SYSIiIiIiOoNFqNkkYjugRYWo0REREREVD31phhNT09Hly5d4OnpCR8fH/Tu3RtZWVlV6uPjjz9GXFwc3NzcEBgYiFGjRuHatWsmYysqKrBw4UJERETA1dUVISEhmDJlCoqKimricOqUBy+MQlVvhg0REREREdlIvagqNm/ejJSUFGRnZ2PYsGEYOnQoDhw4gKSkJPznP/+xqo/XXnsNY8eORVlZGcaPH4+uXbti1apVSEpKwq1bt4zihw4dipkzZ8LLywsTJ05E69atsWTJEvTo0QN3796t6UO0Kd0DV0Z5XZSIiIiIiKrL0dYJVNfdu3cxduxYuLq6Yv/+/YiMjAQAvPLKK0hISMDYsWNx6NChSvs4fvw4Fi1ahNjYWPzwww/QaDQAgF69emHYsGF44403sGTJEiV+y5Yt+PLLL9GjRw9s2bIFDg4OAID58+djzpw5WLZsGSZMmPCIjtgG+MwoERERERHVMLu/Mrpjxw78/PPPeOmll5RCFABatGiBIUOG4PDhwxZv1129ejV0Oh1mzpypFKIA8PLLLyMiIgJr1qxBRUWF0r5y5UoAwLx585RCFACmTZsGLy8vfPbZZzV1eHXCMxc/MGxgMUpERERERNVk91dGMzIyAAApKSlG67p164bly5cjIyMDrVu3fqg+UlJS8NFHH+HkyZOIi4tT4j08PJCYmGgQ6+bmhqSkJGzduhUFBQXw8vKymH90dLTJ9tOnT8PJycns+tqku14MNX6/OlqmzoTLKtvnRUREREREttWsWTNs2LDhoba1+yujOTk5AO59CA/St+ljKuvD09MTDRs2tNhHUVERrl69itDQUKjVxh+ftfu0RKVSwcnJqVp91BR1w0igYQucL3DC+QInuPg1sXVKRNV2/vx5nD9/3tZpENUYjmmqTzieqb7hmDbN7q+MFhQUAIDJq5D6tvz8fIt9+Pv7m1z3YB+V7a8q+9Q7ceKEVXF1gf4qrT3lTGQOxzPVNxzTVJ9wPFN9wzFtmt1fGSUiIiIiIiL7Y/fFqP5KpP6K5f30bd7e3hb7MLW9qT4q219V9klERERERPQ4s/tiNDw8HABM3oOtb9PHVNZHYWEhrl+/brEPDw8PBAQEIDc3FzqdzmI8ERERERERGbP7YrRTp04AgF27dhmt27lzp0HMw/Sxa9cu+Pn5oWXLlgbxRUVFOHjwoEFsaWkp9u3bh7i4OKvepEtERERERPS4svtitFu3bggKCsLnn3+OM2fOKO2nT5/G2rVr0bp1a2Val/Lycpw+fRo//fSTQR/Dhg2DWq3GggULUFJSorSvWbMGZ8+excsvvwxHx9/f9fSnP/0JADBnzhxotVqlfdGiRSgoKMDIkSMfybESERERERHVFyoREcthddumTZvQv39/eHt7Y/DgwQCAdevWobS0FN99950yH2heXh5CQ0ORnJyM9PR0gz5mzJiBd955BxEREejfvz8uX76ML7/8EqGhoTh48CB8fX0N4l944QX885//REJCArp164ZTp05hw4YNaN++Pb777js4OzvXyrETERERERHZo3pRjALAnj17MG/ePPz4449Qq9VISkrCm2++iTZt2igxlRWjIoJPPvkES5cuxblz5+Dt7Y2+ffti4cKFJqd9KS8vx6JFi7By5Ur89NNP8Pf3x/PPP4/58+fDw8PjUR8uERERERGRXas3xSgRERERERHZD7t/ZpSIiIiIiIjsD4tRIiIiIiIiqnUsRomIiIiIiKjWsRglIiIiIiKiWsdilIiIiIiIiGodi1EiIiIiIiKqdSxGiYiIiIiIqNaxGCWL0tPT0aVLF3h6esLHxwe9e/dGVlaWrdOix8j777+Pl156CS1atIBarYZKpcKdO3cq3ebrr79Gu3btoNFo4Ofnh9TUVFy4cMFs/Mcff4y4uDi4ubkhMDAQo0aNwrVr10zGVlRUYOHChYiIiICrqytCQkIwZcoUFBUVVes46fHw888/Y8mSJUhJSUHjxo3h7OyM4OBgjBgxArm5uSa34XimuuzmzZsYP348EhMT4e/vDxcXF4SGhmLQoEE4dOiQyW04psneDBgwACqVCoGBgSbXV/X78qM8B+yKEFVi06ZNolarxdfXV8aNGyfjxo0TX19fcXNzk4MHD9o6PXpMABAA0qRJE/Hx8REAUlpaajb+o48+EgDSuHFjmTRpkowYMULc3NykQYMGcuHCBaP4GTNmCACJiIiQadOmSVpamjg4OEh4eLjcvHnTKP6FF14QAJKQkCDTp0+Xfv36CQB5+umnpaysrEaPneof/XiLioqSMWPGyPTp06Vr164CQHx8fOTYsWMG8RzPVNedOnVKPDw8pEePHvLnP/9ZXnvtNUlLSxONRiNqtVr++c9/GsRzTJO9+fLLL0WtVourq6sEBAQYra/q9+VHfQ7YExajZFZZWZkEBQWJRqOR06dPK+2nTp0SjUYjbdq0sWF29DjZsmWLXL9+XUREkpOTKy1Gr127Ju7u7hIYGChXr15V2vfs2SMqlUoGDBhgEH/s2DFRq9USGxsrxcXFSvvq1asFgEyaNMkgfvPmzQJAevToIRUVFUr7vHnzBID87W9/q/bxUv32zTffyL59+4zalyxZIgCkZ8+eShvHM9mD8vJyKS8vN2o/deqUuLq6SlhYmNLGMU325rfffhN/f3+ZMGGCNGnSxKgYrer35Ud9DtgbFqNk1qZNmwSAjBkzxmjd6NGjBYAcPnzYBpnR48xSMbp06VIBIAsXLjRa1717d3FwcFAKWxGRqVOnCgD54osvjOIjIiLEz8/P4EvWoEGDBIDs37/fILakpES8vLwkLi7uYQ+NHnNarVY0Go24u7srbRzPZO9at24tarVadDqdiHBMk/0ZMmSIBAcHS2FhoclitKrflx/1OWBv+MwomZWRkQEASElJMVrXrVs3gxiiusLSuNVqtcjMzLQqPiUlBTdu3MDJkycN4j08PJCYmGgQ6+bmhqSkJBw9ehQFBQU1ciz0+HF0dISjo6OyzPFM9iwvLw9nz55FVFQUVCoVAI5psi+bN2/G2rVr8dFHH8HDw8NkTFW/Lz/qc8DesBgls3JycgAAzZo1M1qnb9PHENUVVR23OTk58PT0RMOGDS3GFxUV4erVqwgNDYVabfyfT54XVB3//ve/UVBQgK5duyptHM9kT65cuYK5c+di1qxZGD58OFq1agWVSoUPPvhAieGYJntRUFCAsWPHIjU1FX369DEb9zBjuqrx1p4D9sjRcgg9rvR/OfTy8jJap2/Lz8+v1ZyILKnquC0oKIC/v7/Jvh6Mr6xvc/0TWePq1asYP348XFxcMH/+fKWd45nsyZUrVzBv3jxluWHDhli7di26dOmitHFMk72YNm0aiouL8f7771ca9zBjuqrx1p4D9ohXRomIiGyouLgY/fv3x+XLl7F06VLExMTYOiWih/LUU09BRFBWVoaTJ0+iX79+6N27N5YtW2br1IiqJD09HcuXL8eiRYsQEBBg63TqNRajZJb+ry2mnq3Qt3l7e9dqTkSWVHXcenl5mX1+6MH4yvo21z9RZUpLS/Hcc8/hwIEDWLRoEUaNGmWwnuOZ7JGzszOioqLw6aefolu3bnj11Vdx+fJlABzTVPdVVFRg1KhReOaZZzBixAiL8Q8zpqsaX5/HNItRMis8PBwAcP78eaN1+jZ9DFFdUdVxGx4ejsLCQly/ft1ivIeHBwICApCbmwudTmdV/0TmlJWVYcCAAdizZw/mzZuHqVOnGsVwPJO969q1K8rKynDw4EEAHNNU9xUVFeH8+fP47rvvoFaroVKplJ+LFy/i6tWrUKlU8PHxAfBwY7qq8daeA/aIxSiZ1alTJwDArl27jNbt3LnTIIaorrA0bh0cHNChQwer4nft2gU/Pz+0bNnSIL6oqEj5YqVXWlqKffv2IS4uzuzzSkR65eXlSE1NxbZt2zB9+nTMnj3bZBzHM9m7X375BQCUt0RzTFNd5+LigpEjR5r88fDwgJubG0aOHImXX34ZQNW/Lz/qc8Du2HpuGaq77ty5U+kkvq1bt7ZhdvS4sjTPqH4y6UaNGhlMJp2ens4J1alOqKiokNTUVAEg48ePrzSW45nswfHjx6WsrMyoPTs7W7y8vESj0cjNmzdFhGOa7JupeUar+n35UZ8D9obFKFVq48aNolarxdfXV8aNGyfjxo0TX19fcXV1lQMHDtg6PXpMLFy4UIYNGybDhg2TgIAAASBDhw5V2u6fHFpE5MMPPxQA0rhxY5k0aZKMHDlS3NzcpEGDBnLhwgWj/qdPny4AJCIiQqZNmyaDBw8WBwcHCQ8PV75A3U9fSCQkJMiMGTOkX79+AkDat29v8gsZ0f1mz54tAKRBgwYye/ZsmTNnjtHP/Tieqa6bOHGi+Pn5Sf/+/WXixIkyefJk6dOnjzg4OIharZYVK1YYxHNMk70yVYyKVP378qM+B+wJi1GyaPfu3ZKcnCzu7u7i6ekpvXr1kkOHDtk6LXqM6K+GmvvJzc012uarr76Stm3bipubm/j6+sqgQYMkJyfHZP86nU6WLVsmMTEx4uLiIv7+/jJixAiDv1je7+7du/Lmm29KeHi4ODs7S1BQkEyaNEkKCwtr8rCpnho2bFil49nUTUscz1SXZWRkyMsvvyzNmzcXDw8PcXZ2lpCQEElLSzP7h2uOabJH5opRkap/X36U54A9UYmIPKI7gImIiIiIiIhM4guMiIiIiIiIqNaxGCUiIiIiIqJax2KUiIiIiIiIah2LUSIiIiIiIqp1LEaJiIiIiIio1rEYJSIiIiIiolrHYpSIiIiIiIhqHYtRIiIiIiIiqnUsRomIiIiIiKjWsRglIiIiIiKiWsdilIiIiIiIiGodi1EiIrI7nTt3hkqlQnp6uq1ToTpo7ty5UKlUmDt3rkF7eno6VCoVOnfubJO87I1KpYJKpbJ1GkRUj7EYJSKqA/TF1f0/Go0GjRo1Qtu2bTF27Fj861//QkVFha1TpUcgLy/P6Pdv7Q8AiAgyMzPx2muvoWPHjvDz84OTkxMaNmyIHj16YO3atRARk/vWF2j3/zg6OsLX1xdhYWHo168fFixYgIsXL1Z6DMOHD1e2DwoKgk6nMxur0+nQpEkTJX748OEP/dkREZH9crR1AkRE9Lvg4GCEhIQAAMrLy3H79m1kZ2fjxx9/xMcff4zg4GAsX74cPXv2tHGmthUSEoLIyEhoNBpbp1IjXF1dkZSUZNSen5+P48ePA4DJ9Xq7d+9Gt27dlOWwsDCEhoYiNzcXO3bswI4dO/DFF1/g66+/houLi9l+7t9HYWEhfv31V2zcuBEbN27ErFmzMHz4cLz33nvw9PSs9HguX76MXbt2oXv37ibX79mzBz/99FOlfTwKGo0GkZGRyjlGlYuMjLR1CkRUz7EYJSKqQ0aMGGF0a2FpaSl27NiBN954Az/++CN69+6Nf/zjHxg8eLBtkqwD1qxZY+sUalRgYCD27t1r1J6eno4uXboAgMn1eiKC0NBQvPrqq3jxxRfh7++vrPv8888xevRobN68GbNnz8bbb79tth9T+zh//jw+/fRTLFmyBCtWrEBWVhYyMjLg7u5uso8WLVrg9OnTWL16tdlidPXq1QaxtSUxMbFW92fv+FkR0aPG23SJiOo4Nzc39OvXD5mZmRg0aBBEBCNGjLB42yQ9PhITE3HmzBlMmDDBoBAFgJdeegmzZ88GAHz66aeV3j5rSrNmzbBw4ULs2bMHrq6uyMrKwtSpU83G9+zZE/7+/vj2229RWFhotL6oqAjffPMNAgICHvsr/EREjzsWo0REdsLJyQkrV65EgwYNUFZWhnfffddgvVarxYYNGzBq1CjExsbiiSeegKurK8LCwjB69Gjk5OQY9Tl58mSoVCqMHDnS7H7Ly8vRsGFDqFQqZGZmKu23bt3CzJkzERMTA41GA1dXVwQFBaFjx46YO3cubt++bfWxlZaW4u2330ZCQgI8PT3h7OysPC87Y8YM/Pzzzwbx5l5gpH9xzfDhw1FeXo6FCxciKioKrq6u8Pf3x0svvYRLly6ZzUOn02HdunXo1asX/P394eLigqCgIHTr1g0fffQRysrKjLa5ePEixo8fr9w27OXlhXbt2uHDDz+stWd8vby84OTkZHZ97969AQA3b97E9evXH2ofHTp0UK7ar1ixAleuXDEZ5+joiMGDB6OkpATr1683Wr9+/XoUFxdj8ODBcHR8+Bu0CgoKMHXqVDRt2hQuLi4ICQnBuHHjcOPGDbPbVPYCowMHDuC1115DYmIiGjVqBGdnZwQGBqJ///7YvXt3pbmcPn0aqampaNiwITQaDWJiYrB48WJotdpHNlYLCgowb948xMfHw93dHR4eHmjVqhXmz59v8o8AwL3bp8ePH4+IiAi4urpCo9EgJCQEKSkpePvtt1FeXm4Qb+4FRjV57hPRY06IiMjmkpOTBYDMmTPHYuzUqVMFgDRp0sSg/dKlSwJAVCqVBAQESKtWrSQmJkY8PT0FgHh6esr+/fsNtjlx4oQAEA8PDykqKjK5v/Xr1wsAadGihdKWn58vERERAkDUarVERERI27ZtJSgoSBwcHASAZGVlWXXsFRUV0rFjRwEgACQsLEzatm0rTZs2FScnJwEg3377rcE2+s9rz549Bu1z5swRADJ48GDp2rWrAJCIiAiJiYlR+goODpYbN24Y5VFYWCjdu3dX8ggMDJS2bdtKcHCwqNVqASC5ubkG22zcuFHc3d0FgLi5uUlsbKw0bdpUVCqVAJAePXpIWVmZVZ+DKXv27FHyqY59+/Yp/eTn5z/0Pm7duiWOjo4CQFauXGmwbtiwYQJApkyZIllZWQJAkpOTjfro3LmzAJAjR47IlClTBIAMGzasSsfz22+/SXR0tDLeo6OjJTY2VtRqtYSGhsr48eNNnk/6YzWVV7NmzQSA+Pr6SsuWLaVNmzbi7++v7OODDz4wmcvevXsNxkBCQoKEhYUJAPnjH//4SMbqxYsXpXnz5sr5Fxsbqxw/AImMjJRLly4ZbaM/HicnJ4mKipKnnnpKGjVqpIzXW7duGWxjalzU5LlPRMRilIioDqhKMfrvf/9b+ZL466+/Ku23b9+WFStWyLVr1wziy8rK5MMPPxQHBweJjIwUnU5nsD4pKUkAyIoVK0zu79lnnxUAsmjRIqVtyZIlAkDi4uLk4sWLBvG3b9+W5cuXy08//WTxWEREvvnmGwEgQUFBcuzYMYN1JSUlsm7dOsnOzjZot/QF38nJSZo3by5Hjx5V1l28eFGioqIEgMycOdMojxdeeEEASKNGjWT79u0G665fvy6LFi0y+GxPnjwpGo1GHBwc5J133jEoOo8cOSItWrQQAPL6669b9TmYUlPF6CuvvCIAJD4+vtr7aN26tQCQsWPHGrTfX4yKiMTFxYlKpZK8vDwlJi8vT1QqlZLHwxajaWlpAkCaNWsmJ06cUNrPnDkjkZGRSjFXlWJ01apVcubMGaP2Xbt2ib+/vzg5ORmN9eLiYgkODhYA8oc//MGgmNuxY4d4enoqudTkWNWfs/Hx8ZKTk6O0nz17VinSHzzGCRMmCADp1q2bXL9+3WDdr7/+Kn/961+luLjYoN3UuKjJc5+IiMUoEVEdUJViVH/VCYBRkVaZIUOGCAD54YcfDNpXrVolAKRjx45G21y+fFkcHBzEyclJrl69qrSPGTNGAMh7771n9f7NWbhwoQCQV1991eptLBWjAIyuAov8Xvg+WJQdPnxYudJz6NAhq3JITU0VADJ79myT67OyskSlUomXl5eUlpZa1eeDaqIY/fHHH5WrmV988UW199G/f38BIAMGDDBof7AYfffddwWAzJ8/X4mZP3++AJDFixeLyMMVoxcuXFCu5D34+xcR2b9/v3I8VSlGK7N8+XIBIG+99ZZB+2effSYApHHjxlJSUmK03SeffKLkUlNjNT09XRmrp06dMtru6NGjyufz3XffKe09e/YUAPKvf/3L6uM2NS5q8twnIuIzo0REdsbDw0P5t6lnw/bv348ZM2agf//+6Ny5Mzp27IiOHTvi+++/BwBkZWUZxD///PPw9vbG3r17cfbsWYN1q1evhlarRd++fQ1ejKOfGmPTpk0oLi6u1vHo+9q5c2elz/tVRXx8PNq3b2/Urm87f/68Qfs333wDAOjevTvatGljsf+7d+9i48aNAID/+q//MhnTqlUrNG3aFAUFBTh8+HCV8q8pV69excCBA1FRUYEBAwbgxRdfrHaf+vFn7rlEvSFDhsDBwcHgzcdr1qyBo6MjhgwZ8tD737ZtG0QEMTExJp/9bN++PRITEx+q79zcXCxYsACpqano2rWrcu787W9/A2B87vzf//0fACAtLQ1ubm5G/Q0dOhSurq6V7rOqY3Xr1q0A7r0oqkWLFkbbxcbGKm8x1ucH/H6effPNN0bPhlZFTZ77RESc2oWIyM7cXwR4eXkp/y4vL8fw4cOxbt26Srd/sODTaDQYPHgwPvroI6xYsQJvvfWWsm7lypUA7k05c78RI0Zg8eLF2LlzJ5588kn07NkTHTt2RKdOndCqVSuTLz0x5w9/+APCw8Nx/PhxBAcHIyUlBZ06dUKnTp2QmJgIBwcHq/vSa968ucn2gIAAAPfe6Hq/kydPAgCefvppq/o/d+4cSktLoVar8cILL5iN++233wDA6AVMtSE/Px+9e/fGTz/9hISEBKxatapG+tWPv/vHnimBgYHo2bMntmzZgszMTIgIcnJy0KdPH+X38DD00420bNnSbEzLli1x8ODBKvX73nvvYfr06ZUWag+eO/o/3sTHx5uMd3NzQ0REBI4ePWq2z6qO1TNnzgAAYmJizPYZGxuL7du3G0zNMmHCBKxZswZr1qzB1q1b0atXLyQlJSE5OdlkUWtOTZ77RES8MkpEZGfun9Ll/i/1ixYtwrp169CgQQN8+umnyMnJQUlJCeTeIxmYNWsWAJj8sj169GgA965cabVaAEBGRgbOnTuHJ598Unkbq15gYCAOHDiAtLQ0aLVafPXVV5g4cSLatGmDsLAwZR5Ja2g0GmRkZGDMmDFwdXXFpk2bMGPGDHTo0AGNGzfG4sWLqzwdibk5MNVq0//bKygoAAB4e3tb1b/+baE6nQ779u0z+6Mv3EpKSqqUf3UVFRWhV69eyMrKQnR0NLZt22axeLSWfvxZU1AOGzYMwL0r7PoxoW97WPri7MEpbO5X1WI3MzMTkyZNglarxZw5c5CdnY2CggJotVqICHbt2gXA+NzR5+Lp6Wm278rWAVUfq/oxVdkxBgYGGsQC94rXffv2oW/fvigoKMDnn3+OsWPHIioqCrGxsdiyZUuled7fd02d+0REvDJKRGRnMjIyAABNmzY1+EKuvx1y1apV6NOnj9F2ld0C27p1ayQkJODQoUPYunUr+vbtq1wVHTZsmMmrk+Hh4Vi3bh3Ky8tx6NAhZGRk4Ntvv8X+/fsxfPhwaDQaPP/881YdU2BgIJYtW4YPP/wQR48eRUZGBjZt2oTt27dj6tSp0Ol0mDZtmlV9PQx9oZafn29VvP5WVY1GU+duVSwpKUGfPn3www8/oHnz5ti5cyf8/PxqpO9bt27h+PHjAIB27dpZjO/Xrx98fHzw5ZdfAgB8fHzQr1+/auWg/+yvXbtmNubq1atV6lN/7kyePFmZvuZ+5s4da25ZtnQ7c1Xpi9vKjvHXX381iNVLSEjAxo0bUVpaigMHDiAjIwNfffUVjh07hn79+mHv3r0mbxl+UE2e+0T0eOOVUSIiO1JYWKhceXjwS31ubi4A4JlnnjG57Q8//FBp3/qro5999hmKiorw1VdfATC+RfdBTk5OaN++PaZNm4bMzEz8+c9/BgAsW7bMwtEYU6vVaNWqFcaPH49t27bh7bfffui+qkJ/y+P+/futim/evDmcnZ1RUlKifO51wZ07d9CvXz98//33aNKkCXbt2qVcJasJy5Ytg1arhYuLC3r27Gkx3tXVFampqcjPz0d+fj5eeOEFuLi4VCsH/S2l+lurTalsnSkPe+5EREQAgNnbcO/cuWP0HHZ1RUZGAoDyRwFT9OvM3X7r5uaGzp07Y9asWcjOzkafPn2g1WqxfPnyKuVSk+c+ET2eWIwSEdmJ8vJyjBgxAjdu3ICrqyumTp1qsF6j0QAAfvnlF6Ntd+/ebfElOmlpaXB3d8fmzZuxdOlSFBUVITk5GeHh4VXKs0OHDgCAK1euVGm7R91XZQYOHAgA2LFjB44cOWIxXqPRKFefFy9e/ChTs1p5eTn++Mc/YteuXWjcuDF2796N4ODgGus/MzMT8+fPBwCMGjXK6iJ39OjRSElJQUpKivIHj+ro2bMnVCoVjh8/rryU634HDx6s8vOilZ07169fN/u8ba9evQAA//u//4s7d+4YrV+7dq3J9urQ3zK/bds2g2dC9U6cOIHt27cbxFZGpVIpV0Ore57V1vlKRPUHi1EiojqutLQUGzZsQIcOHbB+/XqoVCqsWrXKqNDQX9V59dVXlWcaASA9PR0vvviixbd6enl5ITU1FeXl5crzpeauis6cOROffPKJ0e2LV65cwdKlSwHcuyXQGkuWLMHixYtx+fJlg/Zbt25h0aJFVerrYcXHxyMtLQ06nQ59+vRRnhHU++2337B48WJcv35dafuf//kfuLu74+9//zumT5+OW7duGWxTUlKCb7/9tkYKMEu0Wi0GDx6MLVu2IDAwELt370ZYWFiN9H3hwgXMnDkTXbp0wZ07d5CQkIB33nnH6u2feuop7Ny5Ezt37qyR32NoaKhyC+ioUaMMCrJz585h+PDhcHJyqlKf+nNnwYIFBlcyc3Nz0bdvX7PP/L744osICQnBpUuXMGTIEIPbvHfv3o0pU6ZUORdLkpOT0bFjR+h0OqSlpeHChQvKuvPnzyMtLQ0iguTkZHTq1ElZN2bMGKxbt87otuGzZ88qxbY1v5+aPPeJiDjPKBFRHaCfNzM4OFiSkpIkKSlJ2rVrJ5GRkeLk5KTM9xcSEiLbt2832ceRI0dEo9EIAHF3d5fWrVtLaGioAJDY2FhlTsfK5jLNzMxU9uXl5SXFxcUm4/RzTapUKgkNDZV27dpJVFSUODg4CAAJCgqSvLw8q4594sSJyj6DgoIkMTFRYmJixMXFRQCIj4+P0dyfluYZrWzeSpiYO1FEpKCgQFJSUpT1jRo1krZt20pISIio1WoBILm5uQbbbN++XXx8fASAODo6SnR0tLRr104iIiKUz6JJkyZWfQ6mWDsH6Lp165S4pk2bKmPI1M/hw4fN7uP+uPj4ePH391fWqdVqGTlypBQWFprM4cF5Rq3xMPOMiohcv35dWrRooYzBmJgYiY2NFbVaLaGhoTJ+/PgqzTNaWFgokZGRyu+xZcuWSn9eXl7y/vvvm52fdO/evcp5p9Fo5KmnnpJmzZoJABk4cKA888wzAkC+//57g+2qM1YvXrwo4eHhyu8lLi5OyReAREREyKVLlwy2iY+PFwDi4OAgERER0q5dO2nevLkyJ2lsbKzcvn3b4v5r8twnIuILjIiI6pBLly7h0qVLAO49b+ft7Y34+Hi0adMGzz77LPr27Wt2qpP4+Hjs27cPs2bNQkZGBk6dOoWQkBC89tpreP311/Huu+9a3P/TTz+NqKgonDp1Cmlpacrtiw+aNWsWoqOjkZ6ejosXLyIrKwtOTk6IiYlB3759MXnyZDzxxBNWHfPYsWPh5+eHPXv2ICcnB9nZ2VCr1QgNDUWPHj0wderUGr3d1BxPT09s27YN//jHP7BmzRocOXIE2dnZ8Pf3R9euXTFo0CA8+eSTBtt0794dp0+fxgcffIAtW7YgJycHpaWl8PHxQVJSEnr37q3cAvwolZWVKf/Oy8tDXl6e2djKXtK0b98+APee3fX09MQTTzyB5557Du3bt8fQoUOVOSZtrUGDBvjhhx8wf/58rF+/HmfPnkVAQADGjBmDN954Ax988EGV+vPw8EBGRgb++7//Gxs2bMC5c+fg7++PoUOHYvbs2co5aUpSUhJ+/PFHzJkzB7t378bx48cRFhaGt99+G1OmTFFuga2ptxkD9+b6PHToEJYsWYKvv/4aOTk5AIDo6GgMGjQIkyZNMnp50V//+lds3LgRGRkZ+Pnnn3HhwgW4ubmhbdu2GDhwIMaPH2/2fL9fTZ77REQqERFbJ0FERHVDeXk5goKCcO3aNRw8eBBt27a1dUpEdkur1cLPzw/5+fm4desWfHx8bJ0SEVGdwmdGiYhIsWHDBly7dg1xcXEsRImq6euvv0Z+fj5iY2NZiBIRmcBilIiIAABFRUWYN28eAOCVV16xcTZE9iEzMxOrVq0yesnRhg0bMHbsWADAuHHjbJEaEVGdx9t0iYgec2+99Ra2bNmCkydP4saNG4iKisKRI0fg7Oxs69SI6rz169fj+eefh7OzMyIjI+Hm5oa8vDxcu3YNwL1pg7766iuo1fz7PxHRg/hfRiKix9zp06eRkZEBnU6HAQMGYOvWrSxEiayUmJiIyZMnIyoqCleuXMHhw4dRXl6Ozp07Y+XKlSxEiYgqwSujREREREREVOv4pzoiIiIiIiKqdSxGiYiIiIiIqNaxGCUiIiIiIqJax2KUiIiIiIiIah2LUSIiIiIiIqp1LEaJiIiIiIio1rEYJSIiIiIiolrHYpSIiIiIiIhqHYtRIiIiIiIiqnUsRomIiIiIiKjWsRglIiIiIiKiWsdilIiIiIiIiGodi1EiIiIiIiKqdf8PVtH2SRhe5EQAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 1050x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "\n",
    "# ----------------------------------------------\n",
    "# 1) Preparar tempo e status (0=cens., 1=diálise, 2=morte)\n",
    "# ----------------------------------------------\n",
    "d = df.copy()\n",
    "\n",
    "# tempo até o primeiro evento observável (min ignorando NaN)\n",
    "d[\"time\"] = np.nanmin(\n",
    "    np.vstack([d[\"dialysis_days\"].values,\n",
    "               d[\"death_days\"].values,\n",
    "               d[\"last_visit\"].values]),\n",
    "    axis=0,\n",
    ")\n",
    "\n",
    "# código do evento (1=diálise, 2=morte, 0=censura)\n",
    "def _code_row(r):\n",
    "    if pd.notna(r[\"dialysis_days\"]) and r[\"dialysis_days\"] == r[\"time\"]:\n",
    "        return 1\n",
    "    if pd.notna(r[\"death_days\"]) and r[\"death_days\"] == r[\"time\"]:\n",
    "        return 2\n",
    "    return 0\n",
    "d[\"status\"] = d.apply(_code_row, axis=1)\n",
    "\n",
    "# grupo: estatina no baseline (≤0 d) vs nunca/apos\n",
    "d[\"statin_baseline\"] = (d[\"statin_days\"].fillna(np.inf) <= 0).astype(int)\n",
    "\n",
    "# ----------------------------------------------\n",
    "# 2) CIF de diálise (evento=1), por grupo\n",
    "# ----------------------------------------------\n",
    "fig, ax = plt.subplots(figsize=(7,4))\n",
    "\n",
    "for g, label, ls in [(0, \"No statin at baseline\", \"-\"),\n",
    "                     (1, \"Statin at baseline (≤0 d)\", \"--\")]:\n",
    "    sub = d[d[\"statin_baseline\"]==g]\n",
    "    aj = AalenJohansenFitter()\n",
    "    # status: 0=cens, 1=diálise (evento de interesse), 2=morte (concorrente)\n",
    "    aj.fit(durations=sub[\"time\"], event_observed=sub[\"status\"], event_of_interest=1)\n",
    "    # Em versões recentes, a série abaixo está em aj.cumulative_density_ (nome da coluna pode variar).\n",
    "    ci = aj.cumulative_density_\n",
    "    # Traço PB (sem cores)\n",
    "    ax.step(ci.index, ci.iloc[:,0], where=\"post\", lw=2, label=label)\n",
    "\n",
    "# eixo/legenda\n",
    "ax.set_xlabel(\"Days since T2DM diagnosis\")\n",
    "ax.set_ylabel(\"Cumulative incidence of dialysis\")\n",
    "ax.set_ylim(0, None)\n",
    "ax.legend(frameon=False)\n",
    "ax.grid(False)\n",
    "for s in [\"top\",\"right\"]: ax.spines[s].set_visible(False)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "9c4a3418-be93-4dfb-bab2-2c095a337df7",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.11/site-packages/lifelines/fitters/aalen_johansen_fitter.py:112: Warning: Tied event times were detected. The Aalen-Johansen estimator cannot handle tied event times.\n",
      "                To resolve ties, data is randomly jittered.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1050x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(7,4))\n",
    "\n",
    "# (label, linestyle, color)\n",
    "linhas = [\n",
    "    (\"No statin at baseline\",           \"-\",  \"0.15\"),  # quase preto\n",
    "    (\"Statin at baseline (≤0 d)\",       \"--\", \"0.55\"),  # cinza médio\n",
    "]\n",
    "\n",
    "for (label, ls, col), g in zip(linhas, [0, 1]):\n",
    "    sub = d[d[\"statin_baseline\"]==g]\n",
    "    aj = AalenJohansenFitter()\n",
    "    aj.fit(durations=sub[\"time\"], event_observed=sub[\"status\"], event_of_interest=1)\n",
    "    ci = aj.cumulative_density_\n",
    "    ax.step(ci.index, ci.iloc[:, 0], where=\"post\", lw=2, ls=ls, color=col, label=label)\n",
    "\n",
    "ax.set_xlabel(\"Days since T2DM diagnosis\")\n",
    "ax.set_ylabel(\"Cumulative incidence of dialysis\")\n",
    "ax.legend(frameon=False)\n",
    "for s in [\"top\",\"right\"]: ax.spines[s].set_visible(False)\n",
    "ax.grid(False)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "e99b6f0f-817b-42a5-8737-4e925446478b",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.11/site-packages/lifelines/fitters/aalen_johansen_fitter.py:112: Warning: Tied event times were detected. The Aalen-Johansen estimator cannot handle tied event times.\n",
      "                To resolve ties, data is randomly jittered.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 960x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.11/site-packages/lifelines/fitters/aalen_johansen_fitter.py:112: Warning: Tied event times were detected. The Aalen-Johansen estimator cannot handle tied event times.\n",
      "                To resolve ties, data is randomly jittered.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 960x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# --- Helpers ----------------------------------------------------\n",
    "def build_time_status(df):\n",
    "    d = df.copy()\n",
    "    # \n",
    "    d[\"time\"] = np.nanmin(\n",
    "        np.vstack([d[\"dialysis_days\"].values,\n",
    "                   d[\"death_days\"].values,\n",
    "                   d[\"last_visit\"].values]),\n",
    "        axis=0,\n",
    "    )\n",
    "    # status: 0=cens, 1=diálise, 2=morte\n",
    "    def _code_row(r):\n",
    "        if pd.notna(r[\"dialysis_days\"]) and r[\"dialysis_days\"] == r[\"time\"]:\n",
    "            return 1\n",
    "        if pd.notna(r[\"death_days\"]) and r[\"death_days\"] == r[\"time\"]:\n",
    "            return 2\n",
    "        return 0\n",
    "    d[\"status\"] = d.apply(_code_row, axis=1)\n",
    "    return d\n",
    "\n",
    "def cif_plot_newuser_lag(df, lag_days, fname=None):\n",
    "    d = build_time_status(df)\n",
    "\n",
    "    # grupos\n",
    "    is_new  = d[\"statin_days\"].notna() & (d[\"statin_days\"] > 0)\n",
    "    is_never = d[\"statin_days\"].isna()\n",
    "\n",
    "    # \n",
    "    d_lag = d.copy()\n",
    "    mask_early = is_new & (d_lag[\"time\"] <= d_lag[\"statin_days\"] + lag_days)\n",
    "    d_lag.loc[mask_early, \"time\"] = d_lag.loc[mask_early, \"statin_days\"] + lag_days\n",
    "    d_lag.loc[mask_early, \"status\"] = 0  # censura\n",
    "\n",
    "    # \n",
    "    fig, ax = plt.subplots(figsize=(6.4, 4.0))\n",
    "    for label, subset, ls, col in [\n",
    "        (\"Never users\",              is_never, \"-\",  \"0.15\"),\n",
    "        (f\"New users (lag {lag_days} d)\", is_new,  \"--\", \"0.55\"),\n",
    "    ]:\n",
    "        sub = d_lag.loc[subset, [\"time\",\"status\"]].dropna()\n",
    "        aj = AalenJohansenFitter()\n",
    "        aj.fit(durations=sub[\"time\"], event_observed=sub[\"status\"], event_of_interest=1)\n",
    "        cif = aj.cumulative_density_.iloc[:, 0]  # CIF de diálise\n",
    "        ax.step(cif.index, cif.values, where=\"post\", lw=2, ls=ls, color=col, label=label)\n",
    "\n",
    "    ax.set_xlabel(\"Days since T2DM diagnosis\")\n",
    "    ax.set_ylabel(\"Cumulative incidence of dialysis\")\n",
    "    ax.yaxis.set_major_formatter(PercentFormatter(1.0))\n",
    "    ax.legend(frameon=False)\n",
    "    for s in [\"top\",\"right\"]: ax.spines[s].set_visible(False)\n",
    "    ax.grid(False)\n",
    "\n",
    "    if fname:\n",
    "        plt.savefig(fname, dpi=300, bbox_inches=\"tight\")\n",
    "    plt.show()\n",
    "\n",
    "# --- Execute para 30 e 90 dias ---------------------------------\n",
    "cif_plot_newuser_lag(df, lag_days=30, fname=\"cif_newuser_lag30.pdf\")\n",
    "cif_plot_newuser_lag(df, lag_days=90, fname=\"cif_newuser_lag90.pdf\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "3dc71629-4fdf-41b6-9e02-e0933f40d2a3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Early events censored by lag 30d: 1497\n",
      "Early events censored by lag 90d: 1967\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.11/site-packages/lifelines/fitters/aalen_johansen_fitter.py:112: Warning: Tied event times were detected. The Aalen-Johansen estimator cannot handle tied event times.\n",
      "                To resolve ties, data is randomly jittered.\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.11/site-packages/lifelines/fitters/aalen_johansen_fitter.py:112: Warning: Tied event times were detected. The Aalen-Johansen estimator cannot handle tied event times.\n",
      "                To resolve ties, data is randomly jittered.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1800x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.11/site-packages/lifelines/fitters/aalen_johansen_fitter.py:112: Warning: Tied event times were detected. The Aalen-Johansen estimator cannot handle tied event times.\n",
      "                To resolve ties, data is randomly jittered.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 975x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "# ---------- helpers ----------\n",
    "def prepare_time_status(df):\n",
    "    d = df.copy()\n",
    "    # tempo até primeiro evento observado\n",
    "    d[\"time\"] = np.nanmin(\n",
    "        np.vstack([\n",
    "            d[\"dialysis_days\"].values,\n",
    "            d[\"death_days\"].values,\n",
    "            d[\"last_visit\"].values\n",
    "        ]),\n",
    "        axis=0,\n",
    "    )\n",
    "    # status: 0=cens, 1=diálise, 2=morte\n",
    "    def code(r):\n",
    "        if pd.notna(r[\"dialysis_days\"]) and r[\"dialysis_days\"] == r[\"time\"]:\n",
    "            return 1\n",
    "        if pd.notna(r[\"death_days\"]) and r[\"death_days\"] == r[\"time\"]:\n",
    "            return 2\n",
    "        return 0\n",
    "    d[\"status\"] = d.apply(code, axis=1)\n",
    "    return d\n",
    "\n",
    "def apply_newuser_lag(d, lag_days):\n",
    "    \"\"\"Aplica lag SÓ aos iniciadores (statin_days>0). Retorna cópia e nº de eventos censurados pelo lag.\"\"\"\n",
    "    d = d.copy()\n",
    "    is_new   = d[\"statin_days\"].notna() & (d[\"statin_days\"] > 0)\n",
    "    is_never = d[\"statin_days\"].isna()\n",
    "\n",
    "    # eventos ocorridos até (início + lag) nos iniciadores\n",
    "    early_mask = is_new & (d[\"time\"] <= d[\"statin_days\"] + lag_days)\n",
    "    n_early = int(early_mask.sum())\n",
    "\n",
    "    # censurar esses eventos (mover tempo para início+lag e status=0)\n",
    "    d.loc[early_mask, \"time\"]   = d.loc[early_mask, \"statin_days\"] + lag_days\n",
    "    d.loc[early_mask, \"status\"] = 0\n",
    "\n",
    "    return d, is_never, is_new, n_early\n",
    "\n",
    "def plot_cif(d, mask, label, ax, ls=\"-\", color=\"0.15\"):\n",
    "    sub = d.loc[mask, [\"time\",\"status\"]].dropna()\n",
    "    aj = AalenJohansenFitter()\n",
    "    aj.fit(durations=sub[\"time\"], event_observed=sub[\"status\"], event_of_interest=1)\n",
    "    cif = aj.cumulative_density_.iloc[:,0]\n",
    "    ax.step(cif.index, cif.values, where=\"post\", lw=2, ls=ls, color=color, label=label)\n",
    "\n",
    "# ---------- pipeline ----------\n",
    "D = prepare_time_status(df)\n",
    "\n",
    "# Lag 30\n",
    "D30, never30, new30, n_early30 = apply_newuser_lag(D, lag_days=30)\n",
    "# Lag 90\n",
    "D90, never90, new90, n_early90 = apply_newuser_lag(D, lag_days=90)\n",
    "\n",
    "print(f\"Early events censored by lag 30d: {n_early30}\")\n",
    "print(f\"Early events censored by lag 90d: {n_early90}\")\n",
    "\n",
    "# ---------- figura 1: dois painéis ----------\n",
    "fig, axes = plt.subplots(1, 2, figsize=(12, 4), sharey=True)\n",
    "for ax, Dlag, never, new, ttl in [\n",
    "    (axes[0], D30, never30, new30, \"New users (lag 30 d)\"),\n",
    "    (axes[1], D90, never90, new90, \"New users (lag 90 d)\"),\n",
    "]:\n",
    "    plot_cif(Dlag, never, \"Never users\", ax, ls=\"-\",  color=\"0.15\")\n",
    "    plot_cif(Dlag, new,   ttl,           ax, ls=\"--\", color=\"0.55\")\n",
    "    ax.set_xlabel(\"Days since T2DM diagnosis\")\n",
    "    ax.set_title(ttl, fontsize=10)\n",
    "    for s in [\"top\",\"right\"]: ax.spines[s].set_visible(False)\n",
    "    ax.grid(False)\n",
    "\n",
    "axes[0].set_ylabel(\"Cumulative incidence of dialysis\")\n",
    "for ax in axes: ax.yaxis.set_major_formatter(PercentFormatter(1.0))\n",
    "axes[0].legend(frameon=False)\n",
    "plt.tight_layout()\n",
    "plt.show()\n",
    "\n",
    "# ---------- figura 2: sobreposto (para ver diferença mais claramente) ----------\n",
    "fig, ax = plt.subplots(figsize=(6.5, 4))\n",
    "plot_cif(D30, never30, \"Never users\",        ax, ls=\"-\",  color=\"0.15\")\n",
    "plot_cif(D30, new30,   \"New users (lag 30)\", ax, ls=\"--\", color=\"0.45\")\n",
    "plot_cif(D90, new90,   \"New users (lag 90)\", ax, ls=\":\",  color=\"0.65\")\n",
    "ax.set_xlabel(\"Days since T2DM diagnosis\")\n",
    "ax.set_ylabel(\"Cumulative incidence of dialysis\")\n",
    "ax.yaxis.set_major_formatter(PercentFormatter(1.0))\n",
    "ax.legend(frameon=False)\n",
    "for s in [\"top\",\"right\"]: ax.spines[s].set_visible(False)\n",
    "ax.grid(False)\n",
    "plt.tight_layout()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "15ac4680-6c65-445e-badd-e91cc167c022",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
